AI Code For You

AI Code For You — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Concordancer

  A concordancer is a computer program that automatically constructs a concordance—an alphabetised index of every occurrence of a word or phrase in a body of text, each entry displayed with its surrounding context. Concordancers are primary tools in corpus linguistics, lexicography, computer-assisted translation, and language teaching. The most common display format is the key word in context (KWIC) layout, in which each hit appears centred on a line with a fixed span of words to its left and right, enabling rapid scanning of usage patterns across many occurrences. == History == === Pre-computational concordances === The compilation of concordances predates computers by many centuries. Around 1230, the French Dominican cardinal Hugh of Saint-Cher directed a team of friars in assembling a concordance of the Latin Vulgate Bible, generally regarded as the first systematic concordance of any text. To help readers locate passages, Hugh divided each biblical chapter into lettered sections. Later milestones include a Hebrew Old Testament concordance compiled by Rabbi Mordecai Nathan (1448), Alexander Cruden's Complete Concordance to the Holy Scriptures (1737), and the manuscript Asaf ha-Mazkir, an unfinished concordance to the Babylonian Talmud compiled by Moses Rigotz around the turn of the 19th century. === First computer concordance === The first concordance produced with computing assistance was the Index Thomisticus, a comprehensive lexical index of the writings of and around Thomas Aquinas, totalling approximately 10.6 million Latin words. The Italian Jesuit priest Roberto Busa conceived the project in 1946 and secured the sponsorship of IBM in 1949 after a meeting with chairman Thomas J. Watson. Keypunch operators in Gallarate, Italy, encoded the texts onto punched cards from around 1950. IBM executive Paul Tasman developed the processing methods. The full 56-volume printed edition was completed around 1980, followed by a CD-ROM edition in 1989 and a web-accessible version in 2005. === The KWIC format === The key word in context (KWIC) display was formalised as a computational technique by Hans Peter Luhn, a researcher at IBM, in a 1960 paper in American Documentation. In KWIC output, each instance of the search term (the node word) is centred on a line with a fixed window of words to each side; sorting the resulting lines alphabetically by the immediately adjacent word reveals collocational and phraseological patterns at a glance. === COCOA === One of the first dedicated concordancing programs was COCOA (COunt and COncordance Generation on Atlas), created in 1965 by D. B. Russell at University College London and the Atlas Computer Laboratory in Harwell, Oxfordshire. Written in approximately 4,000 cards of FORTRAN, it processed text annotated with flat, non-hierarchical markup tags and could produce word counts and concordances in multiple languages. Within its first six months COCOA had been applied to texts in at least six languages. A second version designed for multiple mainframe platforms was distributed to British computing centres in the mid-1970s. Growing dissatisfaction with its interface and the eventual withdrawal of Atlas Laboratory support prompted British funding bodies to commission a successor program. === Oxford Concordance Program === The Oxford Concordance Program (OCP) was designed and written in FORTRAN by Susan Hockey and Ian Marriott at Oxford University Computing Services (OUCS) between 1979 and 1980 and first released in 1981. Hockey and Marriott acknowledged that OCP owed much to COCOA and the CLOC system at the University of Birmingham. OCP accepted COCOA-format markup to encode metadata such as author, act, scene, and line number, and was described by its authors as "a machine-independent text analysis program for producing word lists, indices and concordances in a variety of languages and alphabets." By the mid-1980s it had been licensed to approximately 240 institutions in 23 countries. A personal computer version, Micro-OCP, was developed for the IBM PC and sold by Oxford University Press from the late 1980s. Version 2 was rewritten in 1985–86 and documented in the same 1987 article by Hockey and co-author John Martin. === Personal computer era === The availability of affordable personal computers in the 1980s and 1990s enabled standalone concordancing applications that analysts could run locally without specialist computing facilities. MicroConcord, developed by Mike Scott and Tim Johns and published by Oxford University Press in 1993 for MS-DOS, was among the first concordancers designed specifically for classroom language teaching. WordSmith Tools, also developed by Mike Scott, was first released in 1996 and became one of the most widely used corpus analysis suites in academic linguistics research. Other tools from this era include TACT (University of Toronto, 1989), a suite of MS-DOS freeware programs for literary text analysis, and MonoConc, a Windows concordancer created by Michael Barlow. === Web-based concordancers === From the late 1990s onwards, web-based concordancers hosted on remote servers gave researchers browser access to large preloaded corpora without requiring local storage or processing. The Sketch Engine, developed by Adam Kilgarriff and Pavel Rychlý (Masaryk University), was launched commercially in July 2003 by Lexical Computing Limited and introduced word sketches—automatically generated one-page profiles of a word's typical grammatical relations and collocations. AntConc, created by Laurence Anthony at Waseda University, Tokyo, was first released in 2002 as freeware for Windows, macOS, and Linux. == Features == Modern concordancers typically offer a range of analytical functions beyond basic KWIC display. These commonly include: KWIC display with the node word centred and context words in aligned columns, sortable by the word one, two, or three positions to the left or right of the node (L1–L3 and R1–R3) Concordance plots, visualising the distribution of hits as marks along a scaled bar representing each text in the corpus Frequency and word lists, both alphabetical and ranked by frequency Collocation statistics, identifying words that co-occur with the search term more often than chance, quantified by measures such as mutual information, the t-score, or log-likelihood Keyword analysis, comparing word frequencies between a study corpus and a reference corpus to identify statistically distinctive items N-gram analysis, finding frequently recurring word sequences of a specified length Part-of-speech tagging integration, allowing searches filtered to particular grammatical categories Unicode support for multilingual text Bilingual and parallel concordancers additionally display aligned text in two or more languages side by side, enabling comparison of translation equivalents across language pairs. == Notable concordancers == === WordSmith Tools === Created by Mike Scott and first released in 1996, WordSmith Tools is a Windows corpus analysis suite that evolved from MicroConcord. Its three core modules are Concord (KWIC concordances), WordList (frequency and alphabetical word lists), and Keywords (statistical keyword identification relative to a reference corpus). Oxford University Press used WordSmith Tools for dictionary preparation work. Version 4.0 is freely available; later versions are sold by Lexical Analysis Software Limited. === AntConc === AntConc is a freeware, multiplatform concordancing toolkit created by Laurence Anthony, Professor of Applied Linguistics at Waseda University, Tokyo. First released in 2002 and formally described in a 2005 academic paper, it runs on Windows, macOS, and Linux. Its tools include a KWIC concordancer, a concordance plot for visualising distribution across texts, a collocates tool, a keyword list, and an n-gram analysis module. Because it is free and requires only plain text files, AntConc is widely used in linguistics courses and independent research worldwide. === Sketch Engine === The Sketch Engine is a corpus management and query system co-created by Adam Kilgarriff and Pavel Rychlý and launched in 2003 by Lexical Computing Limited. It provides browser-based access to over 800 corpora in more than 100 languages. Beyond concordance searching, it offers word sketches, collocation analysis, distributional thesaurus construction, keyword and terminology extraction, and diachronic analysis. It is used by major publishers including Macmillan and Oxford University Press for lexicographic research. A subset tool, SKELL (Sketch Engine for Language Learning), is freely accessible to individual learners. === Wmatrix === Wmatrix is a web-based corpus processing environment developed by Paul Rayson at the University Centre for Computer Corpus Research on Language (UCREL), Lancaster University. Alongside concordances and frequency lists, Wmatrix integrates CLAWS part-of-speech tagging and the USAS semantic tagger, enabling keyword analysis simultane

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  Transparent decryption

  Transparent decryption is a method of decrypting data which unavoidably produces evidence that the decryption operation has taken place. The idea is to prevent the covert decryption of data. In particular, transparent decryption protocols allow a user Alice to share with Bob the right to access data, in such a way that Bob may decrypt at a time of his choosing, but only while simultaneously leaving evidence for Alice of the fact that decryption occurred. Transparent decryption supports privacy, because this evidence alerts data subjects to the fact that information about them has been decrypted and disincentivises data misuse. Recent work further formalizes transparent decryption and explores practical implementations based on cryptographic protocols and blockchain systems. == Applications == Transparent decryption has been proposed for several systems where there is a need to simultaneously achieve accountability and secrecy. For example: In lawful interception, law enforcement agencies can access private messages and emails. Transparent decryption can make such accesses accountable, giving citizens guarantees about how their private information is accessed. Data arising from vehicles and IoT devices may contain personal information about the vehicle or device owners and their activities. Nevertheless, the data is typically processed in order to provide user functionality and also to investigate and fight crime. Transparent decryption can be used to help users monitor when and how data about them is being accessed and used. == Implementation == In transparent decryption, the decryption key is distributed among a set of agents (called trustees); they use their key share only if the required transparency conditions have been satisfied. Typically, the transparency condition can be formulated as the presence of the decryption request in a distributed ledger. == Alternative solutions == Besides transparent decryption, some other techniques have been proposed for achieving law enforcement while preserving privacy. Solutions that allow competing parties to unify their data access policies. Attribute-based encryption with oblivious attribute translation (OTABE) is an extension of attribute-based encryption that allows translation between proprietary attributes belonging to different organisations, and it has been applied to the problem of law-enforcement access to phone call metadata. Solutions that rely on sophisticated cryptography, such as zero-knowledge proofs that the actions of law enforcement is consistent with judge rulings and the actions of companies, and multi-party computation to compute results.

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  Majal (organization)

  Majal is a regional not-for-profit organization focused on "amplifying voices of dissent" throughout the Middle East and North Africa via digital media. Founded in Bahrain, the organization "creates platforms and web applications that promote freedom of expression and social justice." Majal, which relies on open source platforms, like WordPress and Ruby on Rails, was launched in 2006 by Esra'a Al Shafei as a simple group-blogging idea. However, it has changed course to focus on the development of unique applications and tools. == Objectives and means == Majal's content, in addition to its projects and applications, is free open source content to ensure right to access information for everyone. The organization uses a broad spectrum of social media tools, ranging from written blogs, podcasts, vlogs, comics, video animation and pictures to live broadcasting through radio. == Projects and applications == Majal runs various active projects that include Alliance for Kurdish Rights, The Muslim Network for Baháʼí Rights, a discussion tool for Arab LGBT youth and various Mobile apps. == Funding == Majal is funded through private donations and grants from non-governmental organizations, as well as any potential revenues earned through freelance development. Its primary funders are the Shuttleworth Foundation and the Omidyar Network. In 2008, Majal won the Berkman Award from the Berkman Klein Center for Internet & Society at Harvard University in the Human Rights/Global Advocacy category. This $10,000 award was Majal’s first source of funding. This award is presented to “people or institutions that have made a significant contribution to the Internet and its impact on society over the past decade.” In 2009, the March 18 Movement, a project of Majal, received the Think Social Award, which demonstrates how social media can be used to solve the world’s problems. Esra'a Al-Shafei was named a 2009 Echoing Green Fellow for Civil and Human Rights, a seed funding award for young entrepreneurs engaged in social change. Financially, the fellowship consists of a $60,000 stipend paid over two years. Most recently, MEY has received a grant from the Arab Fund for Arts and Culture for its Mideast Tunes website. == Awards == Winner of Human Rights Tulip 2014 Human Rights Tulip - Human rights - Government.nl Ashoka Changemakers Citizen Media competition in 2011 for their CrowdVoice project. Monaco Media Prize 2011 for Majal founder and director Esra'a Al Shafei in 2011. The BOBS Special Topic Human Rights award in 2011 for the Majal website Migrant Rights. ThinkSocial Award in 2009, as powerful model for how social media can be used to address global problems. Echoing Green, 2009 Fellowship. TEDGlobal 2009 Fellowship. Berkman Award for Internet Innovation from Berkman Klein Center for Internet & Society at Harvard Law School in 2008 for the outstanding contributions to the internet and its impact on society. The Global Journal selected Majal as one of the Top 100 NGOs in 2013. 2013-2014 Shuttleworth Foundation Fellowship. == Leadership == Majal team is led primarily by women. The organization was founded by Esra'a Al Shafei, a blogger from Bahrain in 2006. Ahmed Zidan of Egypt has served for over three years as the Editor-in-Chief of Majal Arabic, and is the co-founder of Ahwaa, and is also a podcaster. Other team members include Mona Kareem, Rima Kalush, Abir Ghattas, Namita Malhotra, and Vani Saraswathi. == 2011 Middle East and North Africa protests == Blogs and video played a role in the documentation of protests throughout the Middle East and North Africa during 2010-2011, also known as the Arab Spring. During this period, MEY's project, CrowdVoice (launched in 2010) helped curate and archive the large amounts of videos, images, and eye-witness reports being aggregated and crowdsourced from across the region. As a result, it had been censored temporarily in Yemen and is still censored in Bahrain. == Media coverage == Majal claims to have received various coverage from news agencies, TV satellite channels, radio stations, newspapers, magazines. For instance, Sky News, CNN, New York Times, BBC, The Guardian, NPR, Time, MTV political blog "Act", VH1, Daily Telegraph, Die Zeit, Frankfurter Rundschau FR-online, Toronto Star, TechCrunch, Rolling Stone Middle East, Abu Dhabi TV, Gulf News, Al-Hasnaa' magazine, ReadWriteWeb, Mashable, The Next Web, Radio Sawt Beirut International, Radio Farda among many others.

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  Codebook

  A codebook is a type of document used for gathering and storing cryptography codes. Originally, codebooks were often literally books, but today "codebook" is a byword for the complete record of a series of codes, regardless of physical format. == Cryptography == In cryptography, a codebook is a document used for implementing a code. A codebook contains a lookup table for coding and decoding; each word or phrase has one or more strings which replace it. To decipher messages written in code, corresponding copies of the codebook must be available at either end. The distribution and physical security of codebooks presents a special difficulty in the use of codes compared to the secret information used in ciphers, the key, which is typically much shorter. The United States National Security Agency documents sometimes use codebook to refer to block ciphers; compare their use of combiner-type algorithm to refer to stream ciphers. Codebooks come in two forms, one-part or two-part: In one-part codes, the plaintext words and phrases and the corresponding code words are in the same alphabetical order. They are organized similar to a standard dictionary. Such codes are half the size of two-part codes but are more vulnerable since an attacker who recovers some code word meanings can often infer the meaning of nearby code words. One-part codes may be used simply to shorten messages for transmission or have their security enhanced with superencryption methods, such as adding a secret number to numeric code words. In two-part codes, one part is for converting plaintext to ciphertext, the other for the opposite purpose. They are usually organized similarly to a language translation dictionary, with plaintext words (in the first part) and ciphertext words (in the second part) presented like dictionary headwords. The earliest known use of a codebook system was by Gabriele de Lavinde in 1379 working for the Antipope Clement VII. Two-part codebooks go back as least as far as Antoine Rossignol in the 1800s. From the 15th century until the middle of the 19th century, nomenclators (named after nomenclator) were the most used cryptographic method. Codebooks with superencryption were the most used cryptographic method of World War I. The JN-25 code used in World War II used a codebook of 30,000 code groups superencrypted with 30,000 random additives. The book used in a book cipher or the book used in a running key cipher can be any book shared by sender and receiver and is different from a cryptographic codebook. == Social sciences == In social sciences, a codebook is a document containing a list of the codes used in a set of data to refer to variables and their values, for example locations, occupations, or clinical diagnoses. == Data compression == Codebooks were also used in 19th- and 20th-century commercial codes for the non-cryptographic purpose of data compression. Codebooks are used in relation to precoding and beamforming in mobile networks such as 5G and LTE. The usage is standardized by 3GPP, for example in the document TS 38.331, NR; Radio Resource Control (RRC); Protocol specification.

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  Irwin Sobel

  Irwin Sobel (born September 12, 1940) is a scientist and researcher in digital image processing. == Biography == Irwin Sobel was born in New York City. He graduated from MIT in 1961 and completed his Ph.D. research at the Stanford Artificial Intelligence Project (SAIL) with thesis Camera Models and Machine Perception. His Ph.D. advisor was Jerome A. Feldman. Starting in 1973, he spent nine years doing postdoctoral research at Columbia University. After 1982, he worked as a Senior Researcher at HP Labs. == Sobel operator == In 1968, Sobel gave a talk entitled "An Isotropic 3x3 Image Gradient Operator" at SAIL; this method became known as the Sobel operator. It was developed jointly with a colleague, Gary Feldman, also at SAIL.

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  Ciphertext expansion

  In cryptography, the term ciphertext expansion refers to the length increase of a message when it is encrypted. Many modern cryptosystems cause some degree of expansion during the encryption process, for instance when the resulting ciphertext must include a message-unique Initialization Vector (IV). Probabilistic encryption schemes cause ciphertext expansion, as the set of possible ciphertexts is necessarily greater than the set of input plaintexts. Certain schemes, such as Cocks Identity Based Encryption, or the Goldwasser-Micali cryptosystem result in ciphertexts hundreds or thousands of times longer than the plaintext. Ciphertext expansion may be offset or increased by other processes which compress or expand the message, e.g., data compression or error correction coding. == Reasons why Ciphertext expansion can occur == === Probabilistic Encryption === Probabilistic encryption schemes, such as the Goldwasser-Micali cryptosystem, necessarily produce ciphertexts that are longer than the original plaintexts. This is because the set of possible ciphertexts must be larger than the set of plaintexts to achieve semantic security. === Initialization Vectors (IVs) === Many block cipher modes of operation, like Cipher Block Chaining (CBC), require the use of an Initialization Vector (IV) that is unique for each message. The IV is typically appended to the ciphertext, resulting in expansion. === Redundancy and Error Correction === Some cryptographic schemes intentionally introduce redundancy or error correction codes into the ciphertext to protect against tampering or transmission errors. This added data increases the ciphertext size. === Specific Cryptosystems === Certain cryptographic schemes, such as Cocks Identity-Based Encryption, can produce ciphertexts that are hundreds or thousands of times longer than the original plaintext. This extreme expansion is a design choice to achieve the desired security properties. Ciphertext expansion can be offset or increased by other processes that compress or expand the message, such as data compression or error correction coding. The overall impact on message size depends on the relative strengths of these competing effects.

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  Social media use in hiring

  Social media use in hiring refers to the examination by employers of job applicants' (public) social media profiles as part of the hiring assessment. For example, the vast majority of Fortune 500 companies use social media as a tool to screen prospective employees and as a tool for talent acquisition. This practice raises ethical questions. Employers and recruiters note that they have access only to information that applicants choose to make public. Many Western-European countries restrict employer's use of social media in the workplace. States including Arkansas, California, Colorado, Illinois, Maryland, Michigan, Nevada, New Jersey, New Mexico, Utah, Washington, and Wisconsin protect applicants and employees from surrendering usernames and passwords for social media accounts. Use of social media has caused significant problems for some applicants who are active on social media. A 2013 survey of 17,000 young people in six countries found that one in ten people aged 16 to 34 claimed to have been rejected for a job because of social media activity. Social media services have been reported to affect deception in resumes. While these services do not affect deception frequency, it does increase deception about interests and hobbies. == Ethical implications == This issue raises many ethical questions that some consider an employer's right and others consider discrimination. As of 2016, except in the states of California, Maryland, and Illinois, there are no laws that prohibit employers from using social media profiles as a basis of whether or not someone should be hired. Title VII also prohibits discrimination during any aspect of employment including hiring or firing, recruitment, or testing. Social media has been integrating into the workplace, and this has led to conflicts within employees and employers.[107] Particularly, Facebook has been seen as a popular platform for employers to investigate in order to learn more about potential employees. This conflict first started in Maryland when an employer requested and received an employee's Facebook username and password. State lawmakers first introduced legislation in 2012 to prohibit employers from requesting passwords to personal social accounts in order to get a job or to keep a job. This led to Canada, Germany, the U.S. Congress and 11 U.S. states to pass or propose legislation that prevents employers' access to private social accounts of employees.[108] Many Western European countries have already implemented laws that restrict the regulation of social media in the workplace. States including Arkansas, California, Colorado, Illinois, Maryland, Michigan, Nevada, New Jersey, New Mexico, Utah, Washington, and Wisconsin have passed legislation that protects potential employees and current employees from employers that demand them to give forth their username or password for a social media account. Laws that forbid employers from disciplining an employee based on activity off the job on social media sites have also been put into act in states including California, Colorado, Connecticut, North Dakota, and New York. Several states have similar laws that protect students in colleges and universities from having to grant access to their social media accounts. Eight states have passed the law that prohibits post secondary institutions from demanding social media login information from any prospective or current students and privacy legislation has been introduced or is pending in at least 36 states as of July 2013. As of May 2014, legislation has been introduced and is in the process of pending in at least 28 states and has been enacted in Maine and Wisconsin. In addition, the National Labor Relations Board has been devoting a lot of their attention to attacking employer policies regarding social media that can discipline employees who seek to speak and post freely on social media sites. Use of social media by young people has caused significant problems for some applicants who are active on social media when they try to enter the job market. A survey of 17,000 young people in six countries in 2013 found that 1 in 10 people aged 16 to 34 have been rejected for a job because of online comments they made on social media websites. A 2014 survey of recruiters found that 93% of them check candidates' social media postings. Moreover, professor Stijn Baert of Ghent University conducted a field experiment in which fictitious job candidates applied for real job vacancies in Belgium. They were identical except in one respect: their Facebook profile photos. It was found that candidates with the most wholesome photos were a lot more likely to receive invitations for job interviews than those with the more controversial photos. In addition, Facebook profile photos had a greater impact on hiring decisions when candidates were highly educated. These cases have created some privacy implications as to whether or not companies should have the right to look at employee's Facebook profiles. In March 2012, Facebook decided they might take legal action against employers for gaining access to employee's profiles through their passwords. According to Facebook Chief Privacy Officer for policy, Erin Egan, the company has worked hard to give its users the tools to control who sees their information. He also said users shouldn't be forced to share private information and communications just to get a job. According to the network's Statement of Rights and Responsibilities, sharing or soliciting a password is a violation of Facebook policy. Employees may still give their password information out to get a job, but according to Erin Egan, Facebook will continue to do their part to protect the privacy and security of their users. == Impacts == Use of social media by young people has caused significant problems for some applicants who are active on social media when they try to enter the job market. A survey of 17,000 young people in six countries in 2013 found that 1 in 10 people aged 16 to 34 have been rejected for a job because of online comments they made on social media websites. A 2014 survey of recruiters found that 93% of them check candidates' social media postings. Moreover, in 2015 professor Stijn Baert of Ghent University conducted a field experiment in which fictitious job candidates applied for real job vacancies in Belgium. They were identical except in one respect: their Facebook profile photos. It was found that candidates with the most wholesome photos were a lot more likely to receive invitations for job interviews than those with the more controversial photos. In addition, Facebook profile photos had a greater impact on hiring decisions when candidates were highly educated. These cases have created some privacy implications as to whether or not companies should have the right to look at employee's Facebook profiles. In March 2012, Facebook decided they might take legal action against employers for gaining access to employee's profiles through their passwords. According to Facebook Chief Privacy Officer for policy, Erin Egan, the company has worked hard to give its users the tools to control who sees their information. He also said users shouldn't be forced to share private information and communications just to get a job. According to the network's Statement of Rights and Responsibilities, sharing or soliciting a password is a violation of Facebook policy. Employees may still give their password information out to get a job, but according to Erin Egan, Facebook will continue to do their part to protect the privacy and security of their users. == Policy Responses == 26 US states now have laws against an employer requiring a current or potential employee to give the employer their username and password.

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  HTTP Strict Transport Security

  HTTP Strict Transport Security (HSTS) is a policy mechanism that helps to protect websites against man-in-the-middle attacks such as protocol downgrade attacks and cookie hijacking. It allows web servers to declare that web browsers (or other complying user agents) should automatically interact with it using only HTTPS connections, which provide Transport Layer Security (TLS/SSL), unlike the insecure HTTP used alone. HSTS is an IETF standards track protocol and is specified in RFC 6797. The HSTS Policy is communicated by the server to the user agent via an HTTP response header field named Strict-Transport-Security. HSTS Policy specifies a period of time during which the user agent should only access the server in a secure fashion. Websites using HSTS often do not accept clear text HTTP, either by rejecting connections over HTTP or systematically redirecting users to HTTPS (though this is not required by the specification). The consequence of this is that a user-agent not capable of doing TLS will not be able to connect to the site. The protection normally only applies after a user has visited the site at least once, relying on the principle of "trust on first use". The way this protection works is that when a user entering or selecting an HTTP (not HTTPS) URL to the site, the client, such as a Web browser, will automatically upgrade to HTTPS without making an HTTP request, thereby preventing any HTTP man-in-the-middle attack from occurring. To counteract this problem, an HSTS preload list maintained by Google Chrome and used by other major web browsers is maintained. If a domain is on this list, the browser skips the initial request and encrypts all communication immediately. Additional domains can be registered at no cost. == Specification history == The HSTS specification was published as RFC 6797 on 19 November 2012 after being approved on 2 October 2012 by the IESG for publication as a Proposed Standard RFC. The authors originally submitted it as an Internet Draft on 17 June 2010. With the conversion to an Internet Draft, the specification name was altered from "Strict Transport Security" (STS) to "HTTP Strict Transport Security", because the specification applies only to HTTP. The HTTP response header field defined in the HSTS specification however remains named "Strict-Transport-Security". The last so-called "community version" of the then-named "STS" specification was published on 18 December 2009, with revisions based on community feedback. The original draft specification by Jeff Hodges from PayPal, Collin Jackson, and Adam Barth was published on 18 September 2009. The HSTS specification is based on original work by Jackson and Barth as described in their paper "ForceHTTPS: Protecting High-Security Web Sites from Network Attacks". Additionally, HSTS is the realization of one facet of an overall vision for improving web security, put forward by Jeff Hodges and Andy Steingruebl in their 2010 paper The Need for Coherent Web Security Policy Framework(s). == HSTS mechanism overview == A server implements an HSTS policy by supplying a header over an HTTPS connection (HSTS headers over HTTP are ignored). For example, a server could send a header such that future requests to the domain for the next year (max-age is specified in seconds; 31,536,000 is equal to one non-leap year) use only HTTPS: Strict-Transport-Security: max-age=31536000. When a web application issues HSTS Policy to user agents, conformant user agents behave as follows: Automatically turn any insecure links referencing the web application into secure links (e.g. http://example.com/some/page/ will be modified to https://example.com/some/page/ before accessing the server). If the security of the connection cannot be ensured (e.g. the server's TLS certificate is not trusted), the user agent must terminate the connection and should not allow the user to access the web application. This helps protect web application users against some passive (eavesdropping) and active network attacks. A man-in-the-middle attacker has a greatly reduced ability to intercept requests and responses between a user and a web application server while the user's browser has HSTS Policy in effect for that web application. == Applicability == The most important security vulnerability that HSTS can fix is SSL-stripping man-in-the-middle attacks, first publicly introduced by Moxie Marlinspike in his 2009 BlackHat Federal talk "New Tricks For Defeating SSL In Practice". The SSL (and TLS) stripping attack works by transparently converting a secure HTTPS connection into a plain HTTP connection. The user can see that the connection is insecure, but crucially there is no way of knowing whether the connection should be secure. At the time of Marlinspike's talk, many websites did not use TLS/SSL, therefore there was no way of knowing (without prior knowledge) whether the use of plain HTTP was due to an attack, or simply because the website had not implemented TLS/SSL. Additionally, no warnings are presented to the user during the downgrade process, making the attack fairly subtle to all but the most vigilant. Marlinspike's sslstrip tool, presented at Black Hat DC 2009, fully automates the attack. HSTS addresses this problem by informing the browser that connections to the site should always use TLS/SSL. The HSTS header can be stripped by the attacker if this is the user's first visit. Google Chrome, Mozilla Firefox, Internet Explorer, and Microsoft Edge attempt to limit this problem by including a "pre-loaded" list of HSTS sites. Unfortunately this solution cannot scale to include all websites on the internet. See limitations, below. HSTS can also help to prevent having one's cookie-based website login credentials stolen by widely available tools such as Firesheep. Because HSTS is time limited, it is sensitive to attacks involving shifting the victim's computer time e.g. using false NTP packets. == Limitations == The initial request remains unprotected from active attacks if it uses an insecure protocol such as plain HTTP or if the URI for the initial request was obtained over an insecure channel. The same applies to the first request after the activity period specified in the advertised HSTS Policy max-age (sites should set a period of several days or months depending on user activity and behavior). === Solutions with preload list === Google Chrome, Mozilla Firefox, and Internet Explorer/Microsoft Edge address this limitation by implementing a "HSTS preloaded list", which is a list that contains known sites supporting HSTS. This list is distributed with the browser so that it uses HTTPS for the initial request to the listed sites as well. As previously mentioned, these pre-loaded lists cannot scale to cover the entire Web. A potential solution might be achieved by using DNS records to declare HSTS Policy, and accessing them securely via DNSSEC, optionally with certificate fingerprints to ensure validity (which requires running a validating resolver to avoid last mile issues). Junade Ali has noted that HSTS is ineffective against the use of false domains; by using DNS-based attacks, it is possible for a man-in-the-middle interceptor to serve traffic from an artificial domain which is not on the HSTS Preload list, this can be made possible by DNS Spoofing Attacks, or simply a domain name that misleadingly resembles the real domain name such as www.example.org instead of www.example.com. Even with an HSTS preloaded list, HSTS cannot prevent advanced attacks against TLS itself, such as the BEAST or CRIME attacks introduced by Juliano Rizzo and Thai Duong. Attacks against TLS itself are orthogonal to HSTS policy enforcement. Neither can it protect against attacks on the server - if someone compromises it, it will happily serve any content over TLS. === Privacy issues === HSTS can be used to near-indelibly tag visiting browsers with recoverable identifying data (supercookies) which can persist in and out of browser "incognito" privacy modes. By creating a web page that makes multiple HTTP requests to selected domains, for example, if twenty browser requests to twenty different domains are used, theoretically over one million visitors can be distinguished (220) due to the resulting requests arriving via HTTP vs. HTTPS; the latter being the previously recorded binary "bits" established earlier via HSTS headers. == Browser support == Chromium and Google Chrome since version 4.0.211.0 Firefox since version 4; with Firefox 17, Mozilla integrates a list of websites supporting HSTS. Opera since version 12 Safari since OS X Mavericks (version 10.9, late 2013) Internet Explorer 11 on Windows 8.1 and Windows 7 with KB3058515 installed (Released as a Windows Update in June 2015) Microsoft Edge and Internet Explorer 11 on Windows 10 BlackBerry 10 Browser and WebView since BlackBerry OS 10.3.3. == Deployment best practices == Depending on the actual deployment there are certain threats (e.g. cookie injection attacks) t

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  Production (computer science)

  In computer science, a production or production rule is a rewrite rule that replaces some symbols with other symbols. A finite set of productions P {\displaystyle P} is the main component in the specification of a formal grammar (specifically a generative grammar). In such grammars, a set of productions is a special case of relation on the set of strings V ∗ {\displaystyle V^{}} (where ∗ {\displaystyle {}^{}} is the Kleene star operator) over a finite set of symbols V {\displaystyle V} called a vocabulary that defines which non-empty strings can be substituted with others. The set of productions is thus a special kind subset P ⊂ V ∗ × V ∗ {\displaystyle P\subset V^{}\times V^{}} and productions are then written in the form u → v {\displaystyle u\to v} to mean that ( u , v ) ∈ P {\displaystyle (u,v)\in P} (not to be confused with → {\displaystyle \to } being used as function notation, since there may be multiple rules for the same u {\displaystyle u} ). Given two subsets A , B ⊂ V ∗ {\displaystyle A,B\subset V^{}} , productions can be restricted to satisfy P ⊂ A × B {\displaystyle P\subset A\times B} , in which case productions are said "to be of the form A → B {\displaystyle A\to B} . Different choices and constructions of A , B {\displaystyle A,B} lead to different types of grammars. In general, any production of the form u → ϵ , {\displaystyle u\to \epsilon ,} where ϵ {\displaystyle \epsilon } is the empty string (sometimes also denoted λ {\displaystyle \lambda } ), is called an erasing rule, while productions that would produce strings out of nowhere, namely of the form ϵ → v , {\displaystyle \epsilon \to v,} are never allowed. In order to allow the production rules to create meaningful sentences, the vocabulary is partitioned into (disjoint) sets Σ {\displaystyle \Sigma } and N {\displaystyle N} providing two different roles: Σ {\displaystyle \Sigma } denotes the terminal symbols known as an alphabet containing the symbols allowed in a sentence; N {\displaystyle N} denotes nonterminal symbols, containing a distinguished start symbol S ∈ N {\displaystyle S\in N} , that are needed together with the production rules to define how to build the sentences. In the most general case of an unrestricted grammar, a production u → v {\displaystyle u\to v} , is allowed to map arbitrary strings u {\displaystyle u} and v {\displaystyle v} in V {\displaystyle V} (terminals and nonterminals), as long as u {\displaystyle u} is not empty. So unrestricted grammars have productions of the form V ∗ ∖ { ϵ } → V ∗ {\displaystyle V^{}\setminus \{\epsilon \}\to V^{}} or if we want to disallow changing finished sentences V ∗ N V ∗ = ( V ∗ ∖ Σ ∗ ) → V ∗ {\displaystyle V^{}NV^{}=(V^{}\setminus \Sigma ^{})\to V^{}} , where V ∗ N V ∗ {\displaystyle V^{}NV^{}} indicates concatenation and forces a non-terminal symbol to always be present on the left-hand side of the productions, and ∖ {\displaystyle \setminus } denotes set minus or set difference. If we do not allow the start symbol to occur in v {\displaystyle v} (the word on the right side), we have to replace V ∗ {\displaystyle V^{}} with ( V ∖ { S } ) ∗ {\displaystyle (V\setminus \{S\})^{}} on the right-hand side. The other types of formal grammar in the Chomsky hierarchy impose additional restrictions on what constitutes a production. Notably in a context-free grammar, the left-hand side of a production must be a single nonterminal symbol. So productions are of the form: N → V ∗ {\displaystyle N\to V^{}} == Grammar generation == To generate a string in the language, one begins with a string consisting of only a single start symbol, and then successively applies the rules (any number of times, in any order) to rewrite this string. This stops when a string containing only terminals is obtained. The language consists of all the strings that can be generated in this manner. Any particular sequence of legal choices taken during this rewriting process yields one particular string in the language. If there are multiple different ways of generating this single string, then the grammar is said to be ambiguous. For example, assume the alphabet consists of a {\displaystyle a} and b {\displaystyle b} , with the start symbol S {\displaystyle S} , and we have the following rules: 1. S → a S b {\displaystyle S\rightarrow aSb} 2. S → b a {\displaystyle S\rightarrow ba} then we start with S {\displaystyle S} , and can choose a rule to apply to it. If we choose rule 1, we replace S {\displaystyle S} with a S b {\displaystyle aSb} and obtain the string a S b {\displaystyle aSb} . If we choose rule 1 again, we replace S {\displaystyle S} with a S b {\displaystyle aSb} and obtain the string a a S b b {\displaystyle aaSbb} . This process is repeated until we only have symbols from the alphabet (i.e., a {\displaystyle a} and b {\displaystyle b} ). If we now choose rule 2, we replace S {\displaystyle S} with b a {\displaystyle ba} and obtain the string a a b a b b {\displaystyle aababb} , and are done. We can write this series of choices more briefly, using symbols: S ⇒ a S b ⇒ a a S b b ⇒ a a b a b b {\displaystyle S\Rightarrow aSb\Rightarrow aaSbb\Rightarrow aababb} . The language of the grammar is the set of all the strings that can be generated using this process: { b a , a b a b , a a b a b b , a a a b a b b b , … } {\displaystyle \{ba,abab,aababb,aaababbb,\dotsc \}} .

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  IBM remote batch terminals

  The IBM 2780 and the IBM 3780 are devices developed by IBM for performing remote job entry (RJE) and other batch functions over telephone lines; they communicate with the mainframe via Binary Synchronous Communications (BSC or Bisync) and replaced older terminals using synchronous transmit-receive (STR). In addition, IBM has developed workstation programs for the 1130, 360/20, 2922, System/360 other than 360/20, System/370 and System/3. == 2780 Data Transmission Terminal == The 2780 Data Transmission Terminal first shipped in 1967. It consists of: A line printer similar to the IBM 1443 that can print up to 240 lines per minute (lpm), or 300 lpm using an extremely restricted character set. A card reader/punch unit, similar to an IBM 1442, that can read up to 400 cards per minute (cpm) and can punch up to 355 cpm. A line buffer that stores data received or to be transmitted over the communications line. A binary synchronous adapter which controls the flow of data over the communications line. The 2780 is capable of local (offline) card to print operation. It comes in four models: Model 1: Can read punched cards and transmit the data to a remote host computer, and can receive and print data sent by the host. Model 2: Same as Model 1 but adds the ability to punch card data received from the host. Model 3: Can only print data received from the host, but not send data to it. Model 4: Can read and punch card data, but has no printing capabilities. The 2780 uses a dedicated communication line at speeds of 1200, 2000, 2400 or 4800 bits per second. It is a half duplex device, although full duplex lines can be used with some increase in throughput. It can communicate in Transcode (a 6-bit code), 8-bit EBCDIC, or 7-bit ASCII. == 2770 Data Communication System == The 2770, announced in 1969, "was said to surpass all other IBM terminals in the variety of available input-output devices." The 2770 was developed by the IBM General Products Division (GPD) in Rochester, MN. It comes standard with a desktop terminal with keyboard. The printer and other devices (any two in any combination) can be attached to the 2772 Multi-Purpose Control unit. Possible devices include: 50 Magnetic Data Inscriber 545 Card Punch Model 3 (non-printing) or Model 4 (printing) 1017 Paper Tape Reader 1018 Paper Tape Punch 1053 Printer Model 1 1255 Magnetic Character Reader Models 1, 2 or 3 2203 Printer Model A1 or A2 2213 Printer Model 1 or 2 2265 Display Station Model 2 2502 Card Reader Model A1 or A2 5496 Data Recorder == 3780 Data Communications Terminal == In May 1972, IBM announced the IBM 3780, an enhanced version of the 2780. The 3780 was developed by IBM's Data Processing Division (DPD). There is one model, with an optional card punch. The 3780 drops Transcode support and incorporates several performance enhancements. It supports compression of blank fields in data using run-length encoding. It provides the ability to interleave data between devices, introduces double buffering, and adds support for the Wait-before-transmit ACKnowledgement (WACK) and Temporary Text Delay (TTD) Binary Synchronous control characters. The integrated punched card unit can read cards at 600 cards per minute. The integrated printer is rated at 300, 350 or 425 lines per minute based on characters set (63, 52 or 39 characters). The 3781 Card Punch is an optional feature. It punches 160 columns per second, or 91 cards per minute if all 80 columns are punched. The IBM 2780 and 3780 were later emulated on various types of equipment, including eventually the personal computer. A notable early emulation was the DN60, by Digital Equipment Corporation in the late 1970s. == 3770 Data Communications System == In 1974 IBM Data Processing Division (DPD) offered a successor to the 3780, called the 3770 Data Communications System, supporting SDLC, BSC, BSC Multi-leaving and SNA, depending on the configuration. The 3770 is a family of desk console style terminals that offers a variety of keyboard and printer combinations as well as I/O equipment attachment and communications features. The terminals come built into a desk and include the following models: 3771 Communication Terminal (optional card reader, optional card punch, wire matrix printer) Models 1 (40 cps printer), 2 (80 cps printer), and 3 (120 cps printer). 3773 Communication Terminal (diskette, wire matrix printer) Models 1 (40 cps printer), 2 (80 cps printer), and 3 (120 cps printer). Each model has a P version which adds some programming features. 3774 Communication Terminal (optional card reader, optional card punch, optional belt printer, wire matrix printer) Models 1 (80 cps printer), and 2 (120 cps printer). Each model has a P version which adds some programming features, a 480-character display and a non-removable diskette. 3775 Communication Terminal (optional card reader, optional card punch, optional diskette, belt printer) Model 1 (120 lpm printer). The model P1 adds some programming features, a 480-character display and a non-removable diskette. 3776 Communication Terminal (optional card reader, optional card punch, optional diskette, belt printer) Models 1 (300 lpm printer) and 2 (400 lpm printer). Models 3 and 4 are similar to models 1 and 2. 3777 Communication Terminal (optional card reader, optional diskette, train printer) Model 1 (up to 1000 lpm printer depending on character set). Model 2 adds an optional card punch, model 3 adds an optional magnetic tape drive and model 4 replaces the train printer with a slower model called the IBM 3262. The model 4 also allows a second, optional, 3262. The following I/O devices can be attached to a 3770 terminal: IBM 2502 Card Reader: Models A1 (up to 150 card per minute), A2 (up to 300 cards per minute) or A3 (up to 400 cards per minute) IBM 3203 Printer Model 3: 1000 LPM using 48 character set IBM 3501 Card Reader: Up to 50 cards per minute desktop unit IBM 3521 Card Punch: Up to 50 cards per minute IBM 3782 Card Attachment unit, which allows the 2502 or 3521 to be attached to any terminal except the 3777 IBM 3784 Line Printer, can be attached to a 3774 as a second printer. Up to 155 LPM with 48 characters set print belt. == Workstation programs == IBM distributes workstation programs with systems software including OS/360 Attached Support Processor (ASP) Houston Automatic Spooling Priority (HASP and HASP II) Operating System/Virtual Storage 1 (OS/VS1) Operating System/Virtual Storage 2 (OS/VS2 MVS) Release 2 through 3.8 MVS versions from MVS/SP Version 1 through z/OS Priority Output Writers, Execution processors and input Readers (POWER) Remote Spooling Communications Subsystem (RSCS) Except for the RJE workstation programs in OS/360, these programs use a variation of BSC known as Multi-leaving. In addition, IBM provides separately ordered workstation programs using BSC. Systems Network Architecture (SNA) and TCP/IP. Workstation programs are available from IBM and third-party vendors to support all of these protocols: 2770/3770 2780/3780 Multileaving Network Job Entry (NJE) OS/360 RJE SNA TCP/IP

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  Data room

  Data rooms are secure spaces used for housing data, usually of a privileged or confidential nature. They can be physical data rooms, virtual data rooms (VDRs), or data centers. They are primarily used for a variety of corporate purposes, including data storage, document exchange, file sharing, financial transactions, and legal proceedings. Today, data rooms are central to workflows in mergers and acquisitions, venture capital, and corporate restructuring, increasingly utilizing artificial intelligence to securely manage and review large datasets. Historically, data rooms were strictly physical locations heavily guarded and monitored. Today, the vast majority of corporate data rooms are hosted virtually on secure cloud platforms, though physical rooms are still occasionally used for highly sensitive government or proprietary intelligence. == Physical Data Rooms == In mergers and acquisitions (M&A), the traditional data room genuinely consists of a physically secured and continually monitored room, normally in the vendor's offices or those of their legal counsel. Bidders and their advisers visit this room in order to inspect and report on various documents, legal contracts, and financial statements made available during the due diligence process. Historically, physical data rooms presented significant logistical challenges. Often, only one bidder at a time was allowed to enter to maintain document integrity and confidentiality. If new documents or new versions of documents were required, they had to be brought in by courier as hardcopies. Teams involved in large due diligence processes typically had to be flown in from many regions or countries and remain available throughout the process. Because these teams comprised a number of experts in different fields—such as legal counsel, forensic accountants, and industry specialists—the overall cost of keeping such groups on call near the physical data room was often extremely high. == Virtual Data Rooms (VDRs) == To address the costs and logistical bottlenecks of physical data rooms, virtual data rooms (VDRs) were developed to provide secure, online dissemination of confidential information. A VDR is essentially a secure cloud repository with strictly controlled access. Access is managed through secure log-ons supplied by the vendor or authority, which can be disabled at any time if a bidder withdraws from a transaction. Because much of the information released during corporate transactions is highly confidential, VDRs utilize digital rights management (DRM) to control information. Restrictions are applied to the viewers' ability to release data to third parties by disabling forwarding, copying, or printing capabilities. Modern VDRs also employ dynamic watermarking and detailed auditing capabilities. Detailed auditing is required for legal reasons so that a precise digital footprint is kept of who has viewed which version of each document, and for how long. Furthermore, modern VDR platforms are typically built to comply with stringent information security standards such as ISO 27001 and SOC 2. Transitioning from sequential physical data rooms to parallel virtual data rooms has been shown to significantly reduce the duration of M&A transactions while allowing sellers to field multiple bidders simultaneously. == Key Applications == Data rooms are commonly used by legal, accounting, investment banking, and private equity firms. Primary applications include: Mergers and Acquisitions (M&A): VDRs are central to the sell-side M&A process. After potential buyers sign a Non-Disclosure Agreement (NDA) and review a Confidential Information Memorandum (CIM), they are granted data room access to perform deep financial due diligence, such as Quality of Earnings (QoE) analysis and legal liability assessments. Venture Capital and Startups: Startups use data rooms as a centralized location for key operational data, capitalization tables, and financial projections to streamline due diligence for angel investors and venture capital firms during fundraising rounds. Initial Public Offerings (IPOs): Taking a company public requires intense regulatory scrutiny. Data rooms are used to securely share company histories and financial audits with investment bankers, legal teams, and regulatory bodies. Corporate Restructuring and Insolvency: During bankruptcies or corporate carve-outs, data rooms are used to organize outstanding debt profiles, creditor agreements, and operational liabilities. == Emerging Technologies == In recent years, the management of virtual data rooms has increasingly incorporated Artificial Intelligence (AI) and Machine Learning (ML). Generative AI and Natural Language Processing (NLP) tools are now integrated into VDRs to automatically index thousands of documents, perform auto-redaction of personally identifiable information (PII), and assist buy-side analysts in identifying hidden liabilities within unstructured text data during the due diligence phase. Modern AI algorithms can extract line items from financial statements to instantly populate structured databases.

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  Data monetization

  Data monetization, a form of monetization, may refer to the act of generating measurable economic benefits from available data sources (analytics). Less commonly, it may also refer to the act of monetizing data services. In the case of analytics, typically, these benefits accrue as revenue or expense savings, but may also include market share or corporate market value gains. Data monetization leverages data generated through business operations, available exogenous data or content, as well as data associated with individual actors such as that collected via electronic devices and sensors participating in the internet of things. For example, the ubiquity of the internet of things is generating location data and other data from sensors and mobile devices at an ever-increasing rate. When this data is collated against traditional databases, the value and utility of both sources of data increases, leading to tremendous potential to mine data for social good, research and discovery, and achievement of business objectives. Closely associated with data monetization are the emerging data as a service models for transactions involving data by the data item. There are three ethical and regulatory vectors involved in data monetization due to the sometimes conflicting interests of actors involved in the digital supply chain. The individual data creator who generates files and records through his own efforts or owns a device such as a sensor or a mobile phone that generates data has a claim to ownership of data. The business entity that generates data in the course of its operations, such as its transactions with financial institutions or risk factors discovered through feedback from customers also has a claim on data captured through their systems and platforms. However, the person that contributed the data may also have a legitimate claim on the data. Internet platforms and service providers, such as Google or Facebook that require a user to forgo some ownership interest in their data in exchange for use of the platform also have a legitimate claim on the data. Thus the practice of data monetization, although common since 2000, is now getting increasing attention from regulators. The European Union and the United States Congress have begun to address these issues. For instance, in the financial services industry, regulations involving data are included in the Gramm–Leach–Bliley Act and Dodd-Frank. Some individual creators of data are shifting to using personal data vaults and implementing vendor relationship management concepts as a reflection of an increasing resistance to their data being federated or aggregated and resold without compensation. Groups such as the Personal Data Ecosystem Consortium, Patient privacy rights, and others are also challenging corporate cooptation of data without compensation. Financial services companies are a relatively good example of an industry focused on generating revenue by leveraging data. Credit card issuers and retail banks use customer transaction data to improve targeting of cross-sell offers. Partners are increasingly promoting merchant based reward programs which leverage a bank’s data and provide discounts to customers at the same time. == Types of data monetization == Internal data monetization - An organization's data is used internally, resulting in economic benefit. This is commonly the case in organizations using analytics to uncover insights, resulting in improved profit, cost savings or the avoidance of risk. Internal data monetization is currently the most common form of monetization, requiring far fewer security, intellectual property, and legal precautions when compared to other types. The potential economic gains from this type of data monetization are limited by the organization's internal structure and situation. External data monetization - A person or organization makes data they possess available on a for-fee basis to external parties, or as a broker for same. This type of monetization is less common and requires various methods to distribute the data to potential buyers and consumers. However, the economic gain that results from collecting data, packaging and distributing it, can be quite large. == Steps == Identification of available data sources – this includes data currently available for monetization as well as other external data sources that may enhance the value of what’s currently available. Connect, aggregate, attribute, validate, authenticate, and exchange data - this allows data to be converted directly into actionable or revenue generating insight or services. Set terms and prices and facilitate data trading - methods for data vetting, storage, and access. For example, many global corporations have locked and siloed data storage infrastructures, which hinders efficient access to data and cooperative and real-time exchange. Perform Research and analytics – draw predictive insights from existing data as a basis for using data for to reduce risk, enhance product development or performance, or improve customer experience or business outcomes. Action and leveraging – the last phase of monetizing data includes determining alternative or improved data centric products, ideas, or services. Examples may include real-time actionable triggered notifications or enhanced channels such as web or mobile response mechanisms. == Pricing variables and factors == A fee for use of a platform to connect buyers and sellers use of a platform to configure, organize, and otherwise process data included in a data trade connecting or including a device or sensor into a data supply chain connecting and credentialing a creator of a data source and a data buyer – often through a federated identity connecting a data source to other data sources to be included in a data supply chain use of an internet service or other transmission services for uploading and downloading data – sometimes, for an individual, through a personal cloud use of encrypted keys to achieve secure data transfer use of a search algorithm specifically designed to tag data sources that contain data points of value to the data buyer linking a data creator or generator to a data collection protocol or form server actions – such as a notification – triggered by an update to a data item or data source included in a data supply chain A price or exchange or other trade value assigned by a data creator or generator to a data item or a data source offered by a data buyer to a data creator assigned by a data buyer for a data item or a data source formatted according to criteria set by a data buyer An incremental fee assigned by a data buyer for a data item or a data set scaled to the reputation of the data creator == Benefits == Improved decision-making that leads to real time crowd sourced research, improved profits, decreased costs, reduced risk and improved compliance More impactful decisions (e.g., make real-time decisions) More timely (lower latency) decisions (e.g., a vendor making purchase recommendations while the customer is still on the phone or in the store, a customer connecting with multiple vendors to discover the best price, triggered notifications when thresholds are reached for data values) More granular decisions (e.g., localized pricing decisions at an individual or device or sensor level versus larger aggregates). Targeted Marketing (e.g., Vendors with access to big data can make targeted advertisements to specific customers within a set data pool decreasing costs for the advertiser and reaching most interested customers) == Frameworks == There are a wide variety of industries, firms and business models related to data monetization. The following frameworks have been offered to help understand the types of business models that are used: Roger Ehrenberg of IA Ventures, a venture capital firm that invests in this sector, has defined three basic types of data product firms: Contributory databases. The magic of these businesses is that a customer provides their own data in exchange for receiving a more robust set of aggregated data back that provides insight into the broader marketplace, or provides a vehicle for expressing a view. Give a little, get a lot back in return – a pretty compelling value proposition, and one that frequently results in a payment from the data contributor in exchange for receiving enriched, aggregated data. Once these contributory databases are developed and customers become reliant on their insights, they become extremely valuable and persistent data assets. Data processing platforms. These businesses create barriers through a combination of complex data architectures, proprietary algorithms, and rich analytics to help customers consume data in whatever form they please. Often these businesses have special relationships with key data providers, that when combined with other data and processed as a whole create valuable differentiation and competitive barriers. Bloomberg is an example of a powerful

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  Ibotta

  Ibotta, Inc. is an American mobile technology company headquartered in Denver, Colorado. Founded in 2011, the company offers cash back rewards on various purchases through its Ibotta Performance Network and direct to consumer app. Ibotta partners with CPG (consumer packaged goods) brands and network publishers to provide these rewards. As of 2024, the company operates solely in the United States. The company's rewards-as-a-service offering, the Ibotta Performance Network, went live in 2022. In August 2019, Ibotta received a $1 billion valuation after its Series D funding, and in 2023, the company surpassed $1.5 billion cash rewards paid to over 50 million consumers since the company's founding. Ibotta became a publicly traded company in April 2024 with a listing on the New York Stock Exchange. As of September 2025, Ibotta is trading at approximately $27.13 per share, marking a 69% decline from its initial public offering price of $88 per share on April 18, 2024. == History == === Founding through early 2019 === Ibotta was founded by current CEO Bryan Leach. The company was incorporated in 2011 and the app launched to both the App Store and Google Play stores in 2012. Early investors included entrepreneur and computer scientist Jim Clark and Tom “TJ” Jermoluk, Chairman of @Home Network. In 2015, Ibotta expanded beyond item level grocery, adding the ability to get cash back on in-store retail purchases. In 2016, in-app mobile commerce began, allowing users to navigate from the Ibotta app to its partners' apps to earn cash back on purchases. In 2016 with a Series C investment, Ibotta had raised over $73 million in funding. In March of that year, Ibotta partnered with Anheuser-Busch to offer cash back for adults who purchased its products. In May, the company partnered with LiveRamp so that companies could use their CRM data to create segmented, personalized campaigns. At the time, the company had around 200 full- and part-time employees and moved from offices in Lower Downtown Denver (LoDo) to a 40,000-square-foot office in the central Denver business district. A year later, the company had to expand to a second floor as it added almost another 100 employees. In 2017, Ibotta added cash back for Uber to its app as well as cash back rewards for online and mobile purchases. In 2018, Ibotta was listed on the Inc. 5,000 list as one of the fastest growing private companies in the U.S. A year later, in January 2019, the Ibotta app had been downloaded more than 30 million times with users receiving a reported $500 million in cash back rewards. That year, Ibotta was the largest mobile company in Colorado with six million monthly active users. === August 2019 to present === In August 2019, Ibotta was valued at $1 billion, following a Series D round of funding. The round was led by Koch Disruptive Technologies, a subsidiary of Koch Industries. 2019 was also the year the company introduced Pay with Ibotta, which allowed users to complete purchases at key retailers on the Ibotta app and earn instant cash back in the process. With that new service, users were able to enter their purchase total and use a QR code to checkout and receive immediate cash back. In 2020, the company partnered with Trees for the Future to plant up to 1 million trees as part of an Earth Month campaign to raise awareness about the waste of unused paper coupons. In response to the COVID-19 pandemic, Ibotta partnered with CPG brands in their “Here to Help” campaign and together committed over $10 million in cash back to American consumers. The company added the ability to earn cash back from online grocery pick-up and delivery orders. Later that year, Ibotta started its free Thanksgiving program, providing users with 100% cash back on select groceries needed for a Thanksgiving meal. By 2022, the company had provided approximately 10 million Thanksgiving meals. In 2021, Ibotta acquired the company OctoShop (originally InStok), a shopping browser extension company. The OctoShop app enables users to compare prices across stores and set restock and price-drop alerts. In April 2022, the Ibotta Performance Network (IPN) was launched. The IPN allows brands to deliver digital offers to consumers through third party publishers. Retailers including Walmart, Dollar General and Family Dollar, food delivery services including Instacart, and convenience stores including Shell are all part of the Ibotta Performance Network. This pay-per-sales or success-based performance network reaches over 200 million consumers. On April 18, 2024, Ibotta had its initial public offering (IPO), trading on the New York Stock Exchange (NYSE) under the ticker symbol IBTA. It was the largest technology IPO in Colorado history. In October 2025, Ibotta announced a partnership with technology and analytics company Circana, integrating Circana's Household Lift measurement into Ibotta campaigns to give CPG brands an increased understanding of the impact of their promotional campaigns. On November 3, 2025, Ibotta launched LiveLift, a tool for companies to measure the return on investment of digital promotions, in order to optimize performance marketing goals. === Athletic partnerships === Ibotta became the official jersey patch partner of the New Orleans Pelicans, a professional men's basketball team in the National Basketball Association (NBA), for the 2020–2021 and 2023–2024 seasons. Ibotta became the official jersey patch partner of the 2023 NBA champion Denver Nuggets baskeetball team beginning in the 2023–2024 season. In March 2023, F1 driver Logan Sargeant, the first U.S. racer to compete in F1 since 2015, partnered with Ibotta. The Ibotta logo was displayed on Sargeant's racing helmet throughout his F1 career. In June 2023, UConn Huskies women's basketball player Paige Bueckers entered into a "name, image, and likeness" (NIL) promotional agreement with Ibotta. According to a press release by Ibotta, the company has agreements with The Brandr Group, which finds NIL opportunities for women college athletes, and the Pearpop social media marketing platform to promote Ibotta. == Legal issues == In April 2025, shareholders filed a class action lawsuit—Fortune v. Ibotta, Inc., in the U.S. District Court for the District of Colorado (Case No. 25-cv-01213)—alleging that the registration statement in connection with Ibotta’s April 2024 initial public offering omitted material information. The complaint claims that, although Ibotta disclosed detailed terms for its contract with Walmart Inc., it failed to warn investors that its agreement with The Kroger Co., its second-largest client, was terminable at will and thus could be canceled without warning, creating a misleading impression of stability.

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  Data governance

  Data governance is a term used on both a macro and a micro level. The former is a political concept and forms part of international relations and Internet governance; the latter is a data management concept and forms part of corporate/organizational data governance. Data governance involves delegating authority over data and exercising that authority through decision-making processes. It plays a role in enhancing the value of data assets. == Macro level == Data governance at the macro level involves regulating cross-border data flows among countries, which is more precisely termed international data governance. This field was first formed in the early 2000s, and consists of "norms, principles and rules governing various types of data." There have been several international groups established by research organizations that aim to grant access to their data. These groups that enable an exchange of data are, as a result, exposed to domestic and international legal interpretations that ultimately decide how data is used. However, as of 2023, there are no international laws or agreements specifically focused on data protection. == Data governance (Data Management) == Data governance is the set of principles, policies, and processes that guide the effective and responsible use of data within an organization. It creates a framework for decision making, accountability, and oversight across the data lifecycle, from creation and storage to sharing and disposal. Data governance is closely linked with data management, which provides the practical methods to carry out governance objectives. These methods include data quality assurance, metadata management, master data management, security controls, and compliance monitoring. Together, governance and management aim to maximize the value of data as a strategic asset, reduce risks from misuse or inaccuracy, and ensure compliance with regulatory, ethical, and business requirements. The importance of this discipline has grown with the rise of big data, cloud computing, and artificial intelligence, where consistent standards and stewardship are essential for privacy protection, interoperability, and informed decision making. == Data governance drivers == While data governance initiatives can be driven by a desire to improve data quality, they are often driven by C-level leaders responding to external regulations. In a recent report conducted by the CIO WaterCooler community, 54% stated the key driver was efficiencies in processes; 39% - regulatory requirements; and only 7% customer service. Examples of these regulations include Sarbanes–Oxley Act, Basel I, Basel II, HIPAA, GDPR, cGMP, and a number of data privacy regulations. To achieve compliance with these regulations, business processes and controls require formal management processes to govern the data subject to these regulations. Successful programs identify drivers that are meaningful to both supervisory and executive leadership. Common themes among the external regulations center on the need to manage risk. The risks can be financial misstatement, inadvertent release of sensitive data, or poor data quality for key decisions. Methods to manage these risks vary from industry to industry. Examples of commonly referenced best practices and guidelines include COBIT, ISO/IEC 38500, and others. The proliferation of regulations and standards creates challenges for data governance professionals, particularly when multiple regulations overlap the data being managed. Organizations often launch data governance initiatives to address these challenges. == Data governance initiatives (Dimensions) == Data governance initiatives improve the quality of data by assigning a team responsible for data's accuracy, completeness, consistency, timeliness, validity, and uniqueness. This team usually consists of executive leadership, project management, line-of-business managers, and data stewards. The team usually employs a methodology for tracking and improving enterprise data, such as Six Sigma, and tools for data mapping, profiling, cleansing, and monitoring data. Data governance initiatives may be aimed at achieving a number of objectives including offering better visibility to internal and external customers (such as supply chain management), compliance with regulatory law, improving operations after rapid company growth or corporate mergers, or to aid the efficiency of enterprise knowledge workers by reducing confusion and error and increasing their scope of knowledge. Many data governance initiatives are also inspired by past attempts to fix information quality at the departmental level, which can lead to incongruent and redundant data quality processes. Most large companies have many applications and databases that can not easily share information. Therefore, knowledge workers within large organizations may not have access to the data they need to best do their jobs. When they do have access to the data, the data quality may be poor. By setting up a data governance practice or corporate data authority (individual or area responsible for determining how to proceed, in the best interest of the business, when a data issue arises), these problems can be mitigated. == Implementation == Implementation of a data governance initiative may vary in scope as well as origin. Sometimes, an executive mandate will arise to initiate an enterprise-wide effort. Sometimes the mandate will be to create a pilot project or projects, limited in scope and objectives, aimed at either resolving existing issues or demonstrating value. Sometimes, an initiative originates from lower down in the organization's hierarchy and will be deployed in a limited scope to demonstrate value to potential sponsors higher up in the organization. The initial scope of an implementation can vary greatly as well, from review of a one-off IT system to a cross-organization initiative. == Data governance tools == Leaders of successful data governance programs declared at the Data Governance Conference in Orlando, FL, in December 2006, that data governance is about 80 to 95 percent communication. That stated, it is a given that many of the objectives of a data governance program must be accomplished with appropriate tools. Many vendors are now positioning their products as data governance tools. Due to the different focus areas of various data governance initiatives, a given tool may or may not be appropriate. Additionally, many tools that are not marketed as governance tools address governance needs and demands.

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  Social media and identity

  Social media can have both positive and negative impacts on a user's identity. Scholars within the fields of psychology and communication study the relationship between social media and identity in order to understand individual behavior, psychological impacts, and social patterns. Communication within political or social groups online can result in practice application, real-world implementation of a concept, of those found identities or the adoption of them as a whole. Young people, defined as emerging adults in or entering college, are especially found to have their identities shaped through social media. Sometimes it seems as though social media is taking over and changing us for the worse. Social media is always changing and can be hard to keep up with. Platforms come and go trends change everyday. What was cool yesterday is lame today. The biggest change from recent years that users are still adjusting to is the name change of Twitter now called X. Since Elon Musk purchased the platform he changed the name but nothing else about the app. Users now feel the need to explain when talking about X. Now it is often referred to as ‘X(Twitter)’ to clarify. == Social Media Usage and Demographics == We know what social media is and how it is used but who uses it? The Pew Research center conducted a 10 year study from 2005-2015 about the demographics of social media usage. While this article is 10 years old the statistics in it are from a very formative time in social media. This is when most people joined and were consistently using social media. Age: While it is no surprise that 90% of young adults use social media they are the main demographic of users. Older adults (65 and older) really hit a boom on social media. In 2005 only 2% of older adults used any form of social media. By 2015 35% of older adults used social media. We can infer that that percentage has grown even more since 2015. Gender: It is known that women tend to use social media more than men. In 2015 it was noted that 65% of women used social media. Men were not far behind, 62% of men were reported to use social media. There are no notable differences of users from various races and ethnicities. The research also shows that more suburban and urban residents use social media over those who live in rural areas. == Young adults == Young adults are especially influenced by social media, where they find social groups to belong to. Research shows that nearly half of teens believe social media platforms has a negative impact on people their age. Psychologists believe that at a time when young adults are coming into adolescence, they are more likely to be influenced by what they see on sites like Instagram or Twitter. Most young adults will widely share, with varying degrees of accuracy, honesty, and openness, information that in the past would have been private or reserved for select individuals. Key questions include whether they accurately portray their identities online and whether the use of social media might impact young adults' identity development. Media Imagery, in particular, is said to be a major influence on the minds of young men and women. Studies have shown that it is even more relevant when it comes to the issue of body image. Social media, in part, has been created to host a safe haven for those who do not claim a solid identity in the material world, but past identities are not easy to escape from since the Internet preserves much of the information that was shared. Social media is an essential part of the social lives of young adults. They rely on it to maintain relationships, create new relationships, and stay up to date with the world around them. Adolescents find social media to be extremely helpful when changing environments, like moving off to university for example. Social media provides students, especially first year students, the opportunity to create the identity they want the world to see. However, it has been seen that these students create online personas that may not reflect their true selves bringing up the issues of impression management. Social media provides young adults with the opportunity to present themselves as something other than their authentic self. Social media providers can help build relationships and community on their platforms. This is something that will create a more positive impact from social media. When young adults interact with each other using social media they are creating something called a social self-identity. Social self identity is what individuals create when they assimilate to being in a group. Social media has gained the reputation of being isolating. If these platforms encourage community then they can help grow users' social self-identity. == Media literacy == The definition of media literacy has evolved over time to encompass a range of experiences that can occur in social media or other digital spaces. The definition of media literacy is also broad and wide ranging in its context. Currently, media literacy is the idea that one is able to analyze, evaluate, and interact with media content in a meaningful way. Educators teach media literacy skills because of the vulnerable relationship that young adults can have with social media. Some examples of media literacy practices, particularly on Twitter, include using hashtags, live tweeting, and sharing information. One of the overall goals of media literacy within the context of social media is to keep young adults aware of potentially violent, graphic, or dangerous content that they may come across on the internet, and how to determine if the content is credible while engaging responsibly with it. In order to be considered media-literate, a person must be able to take in media from online and social platforms and have the correct competencies and context to be able to organize the information. In order to be considered media-literate, the digital information must be given to the user in a way that it can be put into the correct perspective and analyzed, deducted and synthesized.Teenagers and young adults can be vulnerable to specific content online outside of their age-range. Media literacy campaigns and education research shows that targeting those who fall into this age category would be the best way to understand and target their needs as young online users. There are multiple individual studies investigating social media identity relating to media literacy online, however there is a need for much more conclusive information that analyzes multiple studies at a time. Social media literacy is still considered an under-researched topic. Many scholars in media literacy research emphasize the impact of training young adults to consume media in a safe way is the major solution for furthering internet education in children and young adults. The more information the young adults are given on media literacy, the better prepared they are to enter the digital world confidently. One scientific model that has been proposed, known as The Social Media Literacy (SMILE) model is a framework that hypothesizes that at the core of this model it is helping young adults truly know the meaning and display the actions of media literacy online. SMILE is also meant to inspire more research on the subject of media literacy as it relates to social media effects and young adult learning abilities. The model was applied through the lens of a social media positivity bias among adolescents and puts forth five different assumptions about social media and media literacy; Social media literacy as a moderator (what is seen on social media) Social media literacy as a predictor (what is seen for specific individuals on social media) Media literacy within social media is a reciprocal process The development of social media literacy depends on a conditional process of variables affecting other variables Media literacy within social media is a differential learning process, and who teaches it is highly affective of the outcome This model also stresses that human beings learn media literacy (and social media literacy) naturally as they go through life. Research suggests that having young adults taught media literacy from an educator may make them less interested (and therefore less careful) of threats on social media. == Self Presentation == People create images of themselves to present to the public, a process called self presentation. Depending on the demographic, presenting oneself as authentic can result in identity clarity. Methods of self presentation can also be influenced by geography. The framework for this relationship between a user's location and their social media presentation is called the spatial self. Users depict their spatial self in order to include their physical space as a part of their self presentation to an audience. According to a 2018 research paper, patients of plastic surgeons have gone in and asked for specific snapchat "filter" features. This led to a theory of Snap

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