AI Data Privacy Concerns

AI Data Privacy Concerns — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Rake (software)

  Rake is a software task management and a build automation tool created by Jim Weirich. It allows the user to specify tasks and to describe dependencies as well as to group tasks into namespaces. It is similar to SCons and Make. Rake was written in Ruby and has been part of the standard library of Ruby since version 1.9. == Examples == The tasks that should be executed need to be defined in a configuration file called Rakefile. A Rakefile has no special syntax and contains executable Ruby code. === Tasks === The basic unit in Rake is the task. A task has a name and an action block, that defines its functionality. The following code defines a task called greet that will output the text "Hello, Rake!" to the console. When defining a task, you can optionally add dependencies, that is one task can depend on the successful completion of another task. Calling the "seed" task from the following example will first execute the "migrate" task and only then proceed with the execution of the "seed" task.Tasks can also be made more versatile by accepting arguments. For example, the "generate_report" task will take a date as argument. If no argument is supplied the current date is used.A special type of task is the file task, which can be used to specify file creation tasks. The following task, for example, is given two object files, i.e. "a.o" and "b.o", to create an executable program.Another useful tool is the directory convenience method, that can be used to create directories upon demand. === Rules === When a file is named as a prerequisite but it does not have a file task defined for it, Rake will attempt to synthesize a task by looking at a list of rules supplied in the Rakefile. For example, suppose we were trying to invoke task "mycode.o" with no tasks defined for it. If the Rakefile has a rule that looks like this: This rule will synthesize any task that ends in ".o". It has as a prerequisite that a source file with an extension of ".c" must exist. If Rake is able to find a file named "mycode.c", it will automatically create a task that builds "mycode.o" from "mycode.c". If the file "mycode.c" does not exist, Rake will attempt to recursively synthesize a rule for it. When a task is synthesized from a rule, the source attribute of the task is set to the matching source file. This allows users to write rules with actions that reference the source file. === Advanced rules === Any regular expression may be used as the rule pattern. Additionally, a proc may be used to calculate the name of the source file. This allows for complex patterns and sources. The following rule is equivalent to the example above: NOTE: Because of a quirk in Ruby syntax, parentheses are required around a rule when the first argument is a regular expression. The following rule might be used for Java files: === Namespaces === To better organize big Rakefiles, tasks can be grouped into namespaces. Below is an example of a simple Rake recipe:

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  Blocknots

  Blocknots were random sequences of numbers contained in a book and organized by numbered rows and columns and were used as additives in the reciphering of Soviet Union codes, during World War II. The Blocknot consisted of a booklet of fifty sheets of 5-figure random additive, 100 additive groups to a sheet. No sheet was used more than once, thus the blocknots were in effect a form of one-time pad. The Soviet Unions highest grade ciphers that were used in the East, were the 5-figure codebook enciphered with the Blocknot book, and were generally considered unbreakable. == Technical Description == Blocknots were distributed centrally from an office in Moscow. Every Blocknot contained 5-figure groups in a number of sheets, for the enciphering of 5-figure messages. The encipherment was effected by applying additives taken from the pad, of which 50-100 5-figure groups appeared. Each pad had a 5-figure number and each sheet had a 2-figure number running consecutively. There were 5 different types of Blocknots, in two different categories The Individual in which each table of random numbers was used only once. The General in which each page of the Blocknot was valid for one day. The security of the additive sequence rested on the choice of different starting points for each message. In 5-figure messages, the blocknot was one of the first 10 Groups in the message. Its position changed at long intervals, but was always easy to re-identify. The Russians differentiated between three types of blocks: The 3-block, DRIERBLOCK. I-block for Individual Block: 50 pages, additive read off in one direction only. The messages could be used and read only between 2 wireless telegraphy stations on one net. The 6-block, SECHSERBLOCK. Z-block for Circular Block: 30 pages, additive read off in either direction. The messages could be used and read, between all W/T stations in a net. The 2-block, ZWEIERBLOCK. OS-block. Used only in traffic from lower to higher formations. Two other types were used, in lower echelons. Notblock: Used in an emergency. Blocknot used for passing on traffic. The distribution of Blocknots was carried out centrally from Moscow to Army Groups then to Armies. The Army was responsible for their distribution throughout the lower levels of the army down to company level. Independent units took their cipher material with them. Occasionally the same blocknot was distributed to two units on different parts of the front, which enabled Depth to be established. Records of all Blocknots used were kept in Berlin and when a repeat was noticed a BLOCKNOT ANGEBOT message was sent out to all German Signals units, to indicate that it may have been possible to break the code using it. There was no certainty in this. A cryptanalyst with the General der Nachrichtenaufklärung stated while being interrogated by TICOM: It seems that depths of up to 8 were established at the beginning of the Russian Campaign but that no 5-figure code was broken after May 1943 German cryptanalysts who were prisoners of war stated under interrogation, that each of the figures 0 to 9 were placed en clair usually within the first ten groups of the text or sometimes at the end. One indicator was the Blocknot number and the consisted of two random figures, the figure representing the type, and the remaining two, the page of the Blocknot being used. In long messages, 000000 was placed in the message when the end of a page had been reached. == Chi number == The Chi-number was the serial numbering of all 5-figure messages passing through the hands of the Cipher Officer, starting on the first of January and ending on thirty-first December of the current year. It always appeared as the last group in an intercepted message, e.g. 00001 on the 1st January, or when the unit was newly set up. The progression of Chi-numbers was carefully observed and recorded in the form of a graph. A Russian corps had about 10 5-figure messages per day, and Army about 20-30 and a Front about 60–100. After only a relatively short time, the individual curves separated sharply and the type of formation could be recognized by the height of the Chi-number alone. == Monitoring == Blocknots were tracked in a card index, that was maintained by the Signal Intelligence Evaluation Centre (NAAS). The NAAS functionality included evaluation and traffic analysis, cryptanalysis, collation and dissemination of intelligence. The card index, which was one amongst several Card Indexes. A careful recording and study of blocks provided the positive clues in the identification and tracking of formations using 5-figure ciphers. The index was subdivided into two files: Search card index, contained all blocknots and chi-numbers whether or not they were known. Unit card index, contained only known Block and Chi-numbers. Inspector Berger, who was the chief cryptanalyst of NAAS 1 stated that the two files formed: The most important and surest instruments for identifying Russian radio nets, known to him. The Blocknots were also used in the Stationary Intercept Company (Feste), the military unit that were designed to work at a lower level to the NAAS, at the Army level and were semi-motorized, and closer to the front. The Feste used the Blocknot value along with several other parameters to build a network diagram. The network diagram was studied extensively, as part of a 6-stage process, that involved several departments within the Feste. The outcome was a metric which determined the most interesting circuit for traffic monitoring, and least interesting, where monitoring of traffic should cease. == Analysis == Johannes Marquart was a mathematician and cryptanalyst who initially worked for Inspectorate 7/VI and later led Referat Ia of Group IV of the General der Nachrichtenaufklärung. Marquart was assigned the study of the Soviet Union Blocknot traffic. Marquart and his unit conducted extensive research in an attempt to discover the method by which they were produced. All the counts which they made, however, failed to reveal any non-random characteristics in the design of the tables, and while they thought the Blocknots must have been generated by machine, they were never able to draw any concrete deductions as a result of their research. == Example == The Soviet 3rd Guard Tank Army transmits a 5-figure message with the Blocknot of 37581 (one of the first 10 groups in the message). On the same day the Block 37582 was used by the same formation. The next day 37583 appeared. Thereafter, for a period, the Army was not heard by German Wireless telegraphy intercept operators, as it was maintaining wireless silence. After a few days, an unidentified net with the Blocknot 37588 is picked up. This message net is claimed, because of the proximity of the blocks (88/83) to be the 3rd Guard Tank Army. The missing Blocknots 84-87 were presumably used in telegraphic, telephonic or courier communications. The Chi number provides confirmation of the first assumption, based on proximity of blocknots in most cases.

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  Change data capture

  In databases, change data capture (CDC) is a set of software design patterns used to determine and track the data that has changed (the "deltas") so that action can be taken using the changed data. The result is a delta-driven dataset. CDC is an approach to data integration that is based on the identification, capture and delivery of the changes made to enterprise data sources. For instance it can be used for incremental update of data loading. CDC occurs often in data warehouse environments since capturing and preserving the state of data across time is one of the core functions of a data warehouse, but CDC can be utilized in any database or data repository system. == Methodology == System developers can set up CDC mechanisms in a number of ways and in any one or a combination of system layers from application logic down to physical storage. In a simplified CDC context, one computer system has data believed to have changed from a previous point in time, and a second computer system needs to take action based on that changed data. The former is the source, the latter is the target. It is possible that the source and target are the same system physically, but that would not change the design pattern logically. Multiple CDC solutions can exist in a single system. === Timestamps on rows === Tables whose changes must be captured may have a column that represents the time of last change. Names such as LAST_UPDATE, LAST_MODIFIED, etc. are common. Any row in any table that has a timestamp in that column that is more recent than the last time data was captured is considered to have changed. Timestamps on rows are also frequently used for optimistic locking so this column is often available. === Version numbers on rows === Database designers give tables whose changes must be captured a column that contains a version number. Names such as VERSION_NUMBER, etc. are common. One technique is to mark each changed row with a version number. A current version is maintained for the table, or possibly a group of tables. This is stored in a supporting construct such as a reference table. When a change capture occurs, all data with the latest version number is considered to have changed. Once the change capture is complete, the reference table is updated with a new version number. (Do not confuse this technique with row-level versioning used for optimistic locking. For optimistic locking each row has an independent version number, typically a sequential counter. This allows a process to atomically update a row and increment its counter only if another process has not incremented the counter. But CDC cannot use row-level versions to find all changes unless it knows the original "starting" version of every row. This is impractical to maintain.) === Status indicators on rows === This technique can either supplement or complement timestamps and versioning. It can configure an alternative if, for example, a status column is set up on a table row indicating that the row has changed (e.g., a boolean column that, when set to true, indicates that the row has changed). Otherwise, it can act as a complement to the previous methods, indicating that a row, despite having a new version number or a later date, still shouldn't be updated on the target (for example, the data may require human validation). === Time/version/status on rows === This approach combines the three previously discussed methods. As noted, it is not uncommon to see multiple CDC solutions at work in a single system, however, the combination of time, version, and status provides a particularly powerful mechanism and programmers should utilize them as a trio where possible. The three elements are not redundant or superfluous. Using them together allows for such logic as, "Capture all data for version 2.1 that changed between 2005-06-01 00:00 and 2005-07-01 00:00 where the status code indicates it is ready for production." === Triggers on tables === May include a publish/subscribe pattern to communicate the changed data to multiple targets. In this approach, triggers log events that happen to the transactional table into another queue table that can later be "played back". For example, imagine an Accounts table, when transactions are taken against this table, triggers would fire that would then store a history of the event or even the deltas into a separate queue table. The queue table might have schema with the following fields: Id, TableName, RowId, Timestamp, Operation. The data inserted for our Account sample might be: 1, Accounts, 76, 2008-11-02 00:15, Update. More complicated designs might log the actual data that changed. This queue table could then be "played back" to replicate the data from the source system to a target. Data capture offers a challenge in that the structure, contents and use of a transaction log is specific to a database management system. Unlike data access, no standard exists for transaction logs. Most database management systems do not document the internal format of their transaction logs, although some provide programmatic interfaces to their transaction logs (for example: Oracle, DB2, SQL/MP, SQL/MX and SQL Server 2008). Other challenges in using transaction logs for change data capture include: Coordinating the reading of the transaction logs and the archiving of log files (database management software typically archives log files off-line on a regular basis). Translation between physical storage formats that are recorded in the transaction logs and the logical formats typically expected by database users (e.g., some transaction logs save only minimal buffer differences that are not directly useful for change consumers). Dealing with changes to the format of the transaction logs between versions of the database management system. Eliminating uncommitted changes that the database wrote to the transaction log and later rolled back. Dealing with changes to the metadata of tables in the database. CDC solutions based on transaction log files have distinct advantages that include: minimal impact on the database (even more so if one uses log shipping to process the logs on a dedicated host). no need for programmatic changes to the applications that use the database. low latency in acquiring changes. transactional integrity: log scanning can produce a change stream that replays the original transactions in the order they were committed. Such a change stream include changes made to all tables participating in the captured transaction. no need to change the database schema == Confounding factors == As often occurs in complex domains, the final solution to a CDC problem may have to balance many competing concerns. === Unsuitable source systems === Change data capture both increases in complexity and reduces in value if the source system saves metadata changes when the data itself is not modified. For example, some Data models track the user who last looked at but did not change the data in the same structure as the data. This results in noise in the Change Data Capture. === Tracking the capture === Actually tracking the changes depends on the data source. If the data is being persisted in a modern database then Change Data Capture is a simple matter of permissions. Two techniques are in common use: Tracking changes using database triggers Reading the transaction log as, or shortly after, it is written. If the data is not in a modern database, CDC becomes a programming challenge. === Push versus pull === Push: the source process creates a snapshot of changes within its own process and delivers rows downstream. The downstream process uses the snapshot, creates its own subset and delivers them to the next process. Pull: the target that is immediately downstream from the source, prepares a request for data from the source. The downstream target delivers the snapshot to the next target, as in the push model. === Alternatives === Sometimes the slowly changing dimension is used as an alternative method. CDC and SCD are similar in that both methods can detect changes in a data set. The most common forms of SCD are type 1 (overwrite), type 2 (maintain history) or 3 (only previous and current value). SCD 2 can be useful if history is needed in the target system. CDC overwrites in the target system (akin to SCD1), and is ideal when only the changed data needs to arrive at the target, i.e. a delta-driven dataset.

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  Internet Security Alliance

  Internet Security Alliance (ISA) was founded in 2001 as a non-profit collaboration between Carnegie Mellon University's CyLab and Electronic Industries Alliance, a federation of trade associations. The Internet Security Alliance is focused on cyber security, acting as a forum for information sharing and leadership on information security, and lobbying for corporate security interests. == International operations == The Internet Security Alliance operates with a global membership to provide international security for its partners. The organization's membership includes companies located on four continents, and the Executive Committee always includes at least one non-U.S.-based company. The Internet Security Alliance believes that international communication is crucial for long-term greater information security, as it allows for a more realistic approach to addressing the many challenges faced by users of the Internet. == Publications == Published in 2009, The Financial Impact of Cyber Risk is the first known guidance document to attempt to approach the financial impact of cyber risks from the perspective of core business functions. It claims to provide guidance to CFOs and their colleagues responsible for legal issues, business operations and technology, privacy and compliance, risk assessment and insurance, and corporate communications.

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  ARD Sounds

  ARD Sounds (until March 2026: ARD Audiothek) is the joint audio portal of the state broadcasting stations of the ARD and Deutschlandradio on the Internet. The service was officially launched as a mobile app on November 8, 2017, on the occasion of the ARD Radio Play Days in Karlsruhe. A beta web version has also been available since November 2018; it replaces the radio features in the ARD Mediathek, which has since offered only video content. Editorial support for the ARD Audiothek is provided by the ARD, the online editorial team in Mainz. In April 2018, the ARD Audiothek won the German Digital Award in silver in the category "Mobile Apps - User Experience / Usability". Within a year, the mobile app version had been installed more than 510,000 times and had around 21 million audio views. The Android app recorded more than 100,000 downloads in October 2019, according to the Google Play Store.

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  Corporate surveillance

  Corporate surveillance describes the practice of businesses monitoring and extracting information from their users, clients, or staff. This information may consist of online browsing history, email correspondence, phone calls, location data, and other private details. Acts of corporate surveillance frequently look to boost results, detect potential security problems, or adjust advertising strategies. These practices have been criticized for violating ethical standards and invading personal privacy. Critics and privacy activists have called for businesses to incorporate rules and transparency surrounding their monitoring methods to ensure they are not misusing their position of authority or breaching regulatory standards. Monitoring can feel intrusive and give the impression that the business does not promote ethical behavior among its personnel. Staff satisfaction, productivity, and staff turnover may all suffer as a result of the invasion of privacy. == Monitoring methods == Employers may be authorized to gather information through keystroke logging and mouse tracking, which involves recording the keys individuals interact with and cursor position on computers. In cases where employment contracts permit it, they may also monitor webcam activity on company-provided computers. Employers may be able to view the emails sent from business accounts and may be able to see the websites visited when using a corporate internet connection. The screenshot capability is another tool that enables companies to see what remote workers are doing. This feature, which can be found in tracking software, takes screenshots throughout the day at predetermined or arbitrary intervals. Additionally, people who don't work in offices are observed. For instance, it has been claimed that Amazon has incorporated tracking technology to monitor warehouse staff and delivery drivers. == Use of collected information == Information collected by corporations can be used for a variety of uses including marketing research, targeting advertising, fraud detection and prevention, ensuring policy adherence, preventing lawsuits, and safeguarding records and company assets. == Privacy concerns == Concerns over corporate privacy have become more important due to companies collection and manipulation of personal data. Since these practices have been recognized there has been a rising concern about both the security and the possible mishandling of the data accumulated. Social Media data collection and monitoring has been one of the most concerned areas regarding corporate surveillance. Recently, many employers on CareerBuilder have checked their potential candidates' social media activities before the hiring process. This approach can be excusable since it is important to be aware of a future employee or applicant's online presence, and how it might affect the company's reputation in the future. This is crucial since employers are often made legally responsible for their worker's digital actions. These data can also be used to enact political gains. The Facebook-Cambridge Analytica data scandal in 2018 revealed that its British branch to have surreptitiously sold American psychological data to the Trump campaign. This information was supposed to be private, but Facebook's inability to protect user information had reportedly not been a top priority of the company at the time. == Laws and regulations == The National Labor and Relations Act (NLRA) safeguards workplace democracy by giving workers in the private sector the basic freedom to demand better working conditions and choice of representation without fear of retaliation. General Data Protection Regulation (GDPR) outlines the broad responsibilities of data controllers and the "processors" that handle personal data on their behalf. They must adopt the necessary security measures in accordance with the risk involved in the data processing operations they carry out.[1] Electronics Communication Privacy Act (ECPA), as amended, provides protection for electronic, oral, and wire communications while they are being created, while they are being sent, and while they are being stored on computers. Email, phone calls, and electronically stored data are covered by the Act. == Sale of customer data == If it is business intelligence, data collected on individuals and groups can be sold to other corporations, so that they can use it for the aforementioned purpose. It can be used for direct marketing purposes, such as targeted advertisements on Google and Yahoo. These ads are tailored to the individual user of the search engine by analyzing their search history and emails (if they use free webmail services). For example, the world's most popular web search engine stores identifying information for each web search. Google stores an IP address and the search phrase used in a database for up to 2 years. Google also scans the content of emails of users of its Gmail webmail service, in order to create targeted advertising based on what people are talking about in their personal email correspondences. Google is, by far, the largest web advertising agency. Their revenue model is based on receiving payments from advertisers for each page-visit resulting from a visitor clicking on a Google AdWords ad, hosted either on a Google service or a third-party website. Millions of sites place Google's advertising banners and links on their websites, in order to share this profit from visitors who click on the ads. Each page containing Google advertisements adds, reads, and modifies cookies on each visitor's computer. These cookies track the user across all of these sites, and gather information about their web surfing habits, keeping track of which sites they visit, and what they do when they are on these sites. This information, along with the information from their email accounts, and search engine histories, is stored by Google to use for building a profile of the user to deliver better-targeted advertising. == Surveillance of workers == In 1993, David Steingard and Dale Fitzgibbons argued that modern management, far from empowering workers, had features of neo-Taylorism, where teamwork perpetuated surveillance and control. They argued that employees had become their own "thought police" and the team gaze was the equivalent of Bentham's panopticon guard tower. A critical evaluation of the Hawthorne Plant experiments has in turn given rise to the notion of a Hawthorne effect, where workers increase their productivity in response to their awareness of being observed or because they are gratified for being chosen to participate in a project. According to the American Management Association and the ePolicy Institute, who undertook a quantitative survey in 2007 about electronic monitoring and surveillance with approximately 300 US companies, "more than one fourth of employers have fired workers for misusing email and nearly one third have fired employees for misusing the Internet." Furthermore, about 30 percent of the companies had also fired employees for usage of "inappropriate or offensive language" and "viewing, downloading, or uploading inappropriate/offensive content." More than 40 percent of the companies monitor email traffic of their workers, and 66 percent of corporations monitor Internet connections. In addition, most companies use software to block websites such as sites with games, social networking, entertainment, shopping, and sports. The American Management Association and the ePolicy Institute also stress that companies track content that is being written about them, for example by monitoring blogs and social media, and scanning all files that are stored in a filesystem. == Government use of corporate surveillance data == The United States government often gains access to corporate databases, either by producing a warrant for it, or by asking. The Department of Homeland Security has openly stated that it uses data collected from consumer credit and direct marketing agencies—such as Google—for augmenting the profiles of individuals that it is monitoring. The US government has gathered information from grocery store discount card programs, which track customers' shopping patterns and store them in databases, in order to look for terrorists by analyzing shoppers' buying patterns. == Corporate surveillance of citizens == According to Dennis Broeders, "Big Brother is joined by big business". He argues that corporations are in any event interested in data on their potential customers and that placing some forms of surveillance in the hands of companies, results in companies owning video surveillance data for stores and public places. The commercial availability of surveillance systems has led to their rapid spread. Therefore it is almost impossible for citizens to maintain their anonymity. When businesses can monitor their customers, such customers run the risk of facing prejudice when applying for housing, loans, jobs, and other economic opportun

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  Thirst trap

  A thirst trap is a type of social media post intended to entice viewers sexually. It refers to a viewer's "thirst", a colloquialism likening sexual frustration to dehydration, implying desperation, with the afflicted individual being described as "thirsty". The phrase entered into the lexicon in the late 1990s, but is most related to Internet slang that developed in the early 2010s. Its meaning has changed over time, previously referring to a graceless need for approval, affection or attention. == History == The term thirst trap originated within selfie culture, though its precise origins remain unclear. An early use of the phrase with reference to dehydration appears in the 1999 book Running for Dummies by Florence Griffith Joyner and John Hanc, where it referred to the deceptive sensation of thirst being quenched after initial fluid intake, advising continued hydration to avoid the so-called "thirst trap." The modern usage of thirst trap resurfaced around 2011 on platforms such as Twitter and Urban Dictionary, coinciding with the growing popularity of Snapchat, Instagram, and dating apps like Tinder and Grindr. In 2011, Urban Dictionary defined it as "any statement used to intentionally create attention or 'thirst'." By 2018, the term had entered mainstream discourse, appearing in outlets such as The New York Times and GQ without the need for explanation. == Usage of the term == Often, the term thirst trap describes an attractive picture of an individual that they post online. Thirst trap can also describe a digital heartthrob. For instance, former Canadian prime minister Justin Trudeau has been described as a political thirst trap. It has also been described as a modern form of "fishing for compliments". == Motivation == Thirst trapping may be driven by a variety of motives. Individuals often seek attention through "likes" and comments on social media, which can offer a temporary sense of validation and improved self-esteem. It can also serve as an outlet for expressing one's sexuality or enhancing a personal brand. In some cases, sharing such content may provide financial gain. Others might post thirst traps to cope with emotional distress, such as after breakup, or to spite a former lover. Sharing a thirst trap has also been used as a way to connect in times of social isolation (e.g. COVID-19 pandemic). From a physiological standpoint, endorphins and neurotransmitters like oxytocin and dopamine are released during sexual contact. It has been speculated outside of the academic setting that sharing and engaging with thirst traps may elicit similar pleasure responses. == Methodology == Methodologies have developed to take an optimal thirst trap photo. Reporting for Vice magazine, Graham Isador found several of his social network contacts spent a lot of time considering how to take the best photo and what text they should use. They considered angles and lighting. Sometimes they made use of the self-timer feature available on some cameras. Often, body parts are put on display without being too explicit (e.g. bulges of male genitalia, breast cleavage, abdominal muscles, pectoral muscles, backs, buttocks). Often, the thirst trap is accompanied by a caption. For instance, in October 2019, actress Tracee Ellis Ross posted bikini pictures on Instagram with a caption that included the message: "I've worked so hard to feel good in my skin and to build a life that truly matches me and I'm in it and it feels good. ... No filter, no retouch 47 year old thirst trap! Boom!" On Instagram, #ThirstTrapThursdays is a popular tag. Followers reply in turn after a posting. == Variations == "Gatsbying" is a variation of the thirst trap, where one puts posts on social media to attract the attention of a particular individual. The term alludes to the novel The Great Gatsby where the character Jay Gatsby would throw extravagant parties to attract the attention of his love interest, Daisy. "Instagrandstanding" is an alternative name for this. "Wholesome trapping" has developed, where one posts pictures of more meaningful aspects of life, such as spending time with friends or doing outdoor activities. == Criticism == Psychotherapist Lisa Brateman has criticized thirst traps as an unhealthy method of receiving external validation. This desire for external validation can be addictive. Thirst traps can cause pressure to maintain a good physical appearance, and therefore cause self-esteem issues. Additionally, thirst traps are often highly choreographed and thus present a distorted perception of reality. The manufacturing of thirst traps can be limited when one enters a relationship or with time as the body ages. In some cases, thirst traps can lead to harassment and online bullying. In April 2020, model Chrissy Teigen posted a video of herself wearing a black one-piece swimsuit, and she received a multitude of negative comments that constituted bullying and body shaming.

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  Cypherpunks (book)

  Cypherpunks: Freedom and the Future of the Internet is a 2012 book by Julian Assange, in discussion with Internet activists and cypherpunks Jacob Appelbaum, Andy Müller-Maguhn and Jérémie Zimmermann. Its primary topic is society's relationship with information security. In the book, the authors warn that the Internet has become a tool of the police state, and that the world is inadvertently heading toward a form of totalitarianism. They promote the use of cryptography to protect against state surveillance. In the introduction, Assange says that the book is "not a manifesto [...] [but] a warning". He told Guardian journalist Decca Aitkenhead: A well-defined mathematical algorithm can encrypt something quickly, but to decrypt it would take billions of years – or trillions of dollars' worth of electricity to drive the computer. So cryptography is the essential building block of independence for organisations on the Internet, just like armies are the essential building blocks of states, because otherwise one state just takes over another. There is no other way for our intellectual life to gain proper independence from the security guards of the world, the people who control physical reality. Assange later wrote in The Guardian: "Strong cryptography is a vital tool in fighting state oppression." saying that was the message of his book, Cypherpunks. Cypherpunks is published by OR Books. It is primarily a transcript of World Tomorrow episode eight, a two-part interview between Assange, Jacob Appelbaum, Andy Müller-Maguhn, and Jérémie Zimmermann. In the foreword, Assange said, "the Internet, our greatest tool for emancipation, has been transformed into the most dangerous facilitator of totalitarianism we have ever seen".

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  Digital Michelangelo Project

  The Digital Michelangelo Project was a pioneering initiative undertaken during the 1998–1999 academic year to digitize the sculptures and architecture of Michelangelo using advanced laser scanning technology. The project was led by a team of 30 faculty, staff, and students from Stanford University and the University of Washington, with the aim of creating high-resolution 3D models of Michelangelo's works for scholarly, educational, and preservation purposes. == Objectives == The primary goals of the Digital Michelangelo Project were: To apply recent advancements in laser rangefinder technology for digitizing large cultural artifacts. To create detailed digital archives of Michelangelo's sculptures and architectural spaces for future study and analysis. To explore potential educational and curatorial applications for 3D scanned data. === Artworks digitized === The project involved scanning several iconic works by Michelangelo, including: David The Unfinished Slaves (Atlas, Awakening, Bearded, and Youthful) St. Matthew The allegorical statues from the Medici tombs (Night, Day, Dawn, and Dusk) The architectural interiors of the Tribuna del David at the Galleria dell'Accademia and the New Sacristy in the Medici Chapels. == Technology and methodology == === 3D scanning === The project's primary scanner was a laser triangulation rangefinder mounted on a motorized gantry, custom-built by Cyberware Inc. The scanner used a laser sheet to project onto an object, capturing its shape through triangulation. Multiple scans were taken from various angles and combined into a single, detailed 3D mesh. The resolution achieved was fine enough to capture even Michelangelo's chisel marks, with triangles approximately 0.25 mm on each side. In addition to shape data, color data was captured using a spotlight and a secondary camera, enabling the creation of textured 3D models. === Data processing === The project developed a software suite for processing the scanned data. This included: Aligning and merging multiple scans into a seamless 3D model. Filling holes in the geometry caused by inaccessible areas. Correcting color data for lighting inconsistencies and shadowing. Non-photorealistic rendering techniques were also applied, highlighting surface features such as Michelangelo’s chisel marks for enhanced visualization. == Logistical challenges == The scale and complexity of the project presented several challenges: Data size: The dataset for David alone comprised 2 billion polygons and 7,000 color images, occupying 60 GB of storage. Artifact safety: Ensuring the safety of the statues during scanning required extensive crew training, foam-encased equipment, and collision-prevention mechanisms. == Applications and impact == The digitized models have numerous potential applications: Art history: Allowing precise measurements and geometric analysis, such as determining chisel types or evaluating structural balance. Education: Providing new ways to study art, including interactive viewing from unconventional angles and with custom lighting. Museum curation: Enhancing visitor experiences through interactive kiosks and virtual models. The project demonstrated the potential for 3D technology to preserve and disseminate cultural heritage. == Data distribution == The project's models are available through Stanford University for scholarly purposes, under strict licensing due to Italian intellectual property laws. === ScanView === To provide public access to the 3D models while respecting usage restrictions, the project developed ScanView, a client/server rendering system. ScanView allows users to view and interact with high-resolution 3D models without downloading the data. The client component consists of a freely available viewer program and simplified 3D models. Users can navigate these models locally, adjusting position, orientation, lighting, and surface appearance. When a user finalizes a view, the client queries a remote server for a high-resolution rendering of the model, which is sent back to overwrite the simplified version on the user’s screen. A typical query-response cycle takes 1–2 seconds, depending on network conditions. To protect the models from unauthorized reconstruction, the system employs several security measures, including: Encrypting queries Perturbing viewpoint and lighting parameters Adding noise and warping rendered images Compressing images before transmission ScanView operates on Windows-based PCs and provides access to selected models, including David and St. Matthew, as well as other artifacts such as fragments of the Forma Urbis Romae and items from the Stanford 3D Scanning Repository. == Sponsors == The Digital Michelangelo Project was supported by Stanford University, Interval Research Corporation, and the Paul G. Allen Foundation for the Arts.

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  Social media surgery

  A social media surgery is a gathering at which volunteer "surgeons" with expertise in using web tools, chiefly social media, offer free advice in using such tools, to representatives ("patients") of non-profit organisations, charities, community groups and activists, with "no boring speeches or jargon". The idea was conceived by Pete Ashton, with Nick Booth of Podnosh Ltd, who ran the first such surgery in Birmingham, England, on 15 October 2008. In July 2009, a spin-off surgery (dubbed the "Social media mob") started in Mosman, Australia, and in January 2010, the first spin-off surgery in Africa was held. On 16 February 2012, it was announced that the Social Media Surgery movement had won "the Prime Minister’s Big Society Award". Prime Minister David Cameron said: This is an excellent initiative - such a simple idea and yet so effective. The popularity of these surgeries and the fact that they have inspired so many others across the country to follow in their footsteps, is testament to its brilliance. Congratulations to Nick and all the volunteers who have shared their time and expertise to help so many local groups make the most of the internet to support their community. A great example of the Big Society in action. The scheme also won the 2013 Adult Learners' Week "BBC Learning Through Technology Award".

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  Cambridge Semantics

  Cambridge Semantics is a privately held company headquartered in Boston, Massachusetts with an office in San Diego, California. The company is an enterprise big data management and exploratory analytics software company. == History == Cambridge Semantics was founded in 2007 by Sean Martin, Lee Feigenbaum, Simon Martin, Rouben Meschian, Ben Szekely and Emmett Eldred who all previously worked at IBM's Advanced Technology Internet Group. In 2012, Cambridge Semantics appointed Chuck Pieper as chief executive. Pieper was previously at Credit Suisse. In January 2016, Cambridge Semantics acquired SPARQL City and its graph database intellectual property. On April 18, 2024, Altair Engineering acquired Cambridge Semantics. On 26 March 2025, Siemens announced the acquisition of Altair. == Products == Anzo Smart Data Lake uses Semantic Web Technologies. It allows IT departments and their business users to access data. AnzoGraph DB Graph database. AnzoGraph DB is a massively parallel processing (MPP) native graph database built for diverse data harmonization and analytics at scale (trillions of triples and more), speed and deep link insights. It is used for embedded analytics that require graph algorithms, graph views, named queries, aggregates, geospatial, built-in data science functions, data warehouse-style BI and reporting functions. It allows users to load and query RDF data using SPARQL or Cypher for OLAP-style analytics. == Marketing == Cambridge Semantics named SIIA Codie award 2018 finalist. Cambridge Semantics named 2018 Gold Stevie Award Winner for 'Big Data Solutions'. Cambridge Semantics named KMWorld’s 2018 ‘100 Companies That Matter in Knowledge Management’. Cambridge Semantics named to Database Trends and Applications' 'Trend-Setting Products in Data and Information Management for 2018'. Cambridge Semantics named to KMWorld Trend-Setting Products of 2017. Cambridge Semantics named to Database Trends and Applications 'DBTA 100: The Companies That Matter Most in Data'. Cambridge Semantics named SIIA Codie award 2017 winner for ‘Best Text Analytics and Semantic Technology Solution’. Cambridge Semantics named 2017 Silver Stevie Award Winner for 'Big Data Solutions'. Cambridge Semantics named KMWorld’s 2017 ‘100 Companies That Matter in Knowledge Management’. Cambridge Semantics named SIIA Codie award 2016 finalist. Cambridge Semantics named KMWorld’s 2016 ‘100 Companies That Matter in Knowledge Management’ and KMWorld Trend-Setting Products of 2015. Cambridge Semantics named 2016 Bio-IT World Best of Show People's Choice Award Contenders and 2015 Bio-IT best of show finalist. Anzo Insider Trading Investigation and Surveillance named 2015 CODiE Award finalist. Cambridge Semantics Selected as Finalist for 2014 MIT Sloan CIO Symposium's Innovation Showcase. Cambridge Semantics named SIIA CODiE Award 2014 finalist. Cambridge Semantics Win 2013 SIIA CODiE Award for best business intelligence and analytics solution. Cambridge Semantics wins KMWorld 2012 Promise Award. Cambridge Semantics wins Best of Show at 2012 Bio-IT World Conference.

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  Blinding (cryptography)

  In cryptography, blinding first became known in the context of blind signatures, where the message author blinds the message with a random blinding factor, the signer then signs it and the message author "unblinds" it; signer and message author are different parties. Since the late 1990s, blinding mostly refers to countermeasures against side-channel attacks on encryption devices, where the random blinding and the "unblinding" happen on the encryption devices. The techniques used for blinding signatures were adapted to prevent attackers from knowing the input to the modular exponentiation function for Diffie-Hellman or RSA. Blinding must be applied with care, for example Rabin–Williams signatures. If blinding is applied to the formatted message but the random value does not honor Jacobi requirements on p and q, then it could lead to private key recovery. A demonstration of the recovery can be seen in CVE-2015-2141 discovered by Evgeny Sidorov. Side-channel attacks allow an adversary to recover information about the input to a cryptographic operation within an asymmetric encryption scheme, by measuring something other than the algorithm's result, e.g., power consumption, computation time, or radio-frequency emanations by a device. Typically these attacks depend on the attacker knowing the characteristics of the algorithm, as well as (some) inputs. In this setting, blinding serves to alter the algorithm's input into some unpredictable state. Depending on the characteristics of the blinding function, this can prevent some or all leakage of useful information. Note that security depends also on the resistance of the blinding functions themselves to side-channel attacks. == Examples == In RSA blinding involves computing the blinding operation E(x) = (xr)e mod N, where r is a random integer between 1 and N and relatively prime to N (i.e. gcd(r, N) = 1), x is the plaintext, e is the public RSA exponent and N is the RSA modulus. As usual, the decryption function f(z) = zd mod N is applied thus giving f(E(x)) = (xr)ed mod N = xr mod N. Finally it is unblinded using the function D(z) = zr−1 mod N. Multiplying xr mod N by r−1 mod N yields x, as desired. When decrypting in this manner, an adversary who is able to measure time taken by this operation would not be able to make use of this information (by applying timing attacks RSA is known to be vulnerable to) as they does not know the constant r and hence has no knowledge of the real input fed to the RSA primitives. Blinding in GPG 1.x

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  Message queuing service

  A message queueing service is a message-oriented middleware or MOM deployed in a compute cloud using software as a service model. Service subscribers access queues and or topics to exchange data using point-to-point or publish and subscribe patterns. It's important to differentiate between event-driven and message-driven (aka queue driven) services: Event-driven services (e.g. AWS SNS) are decoupled from their consumers. Whereas queue / message driven services (e.g. AWS SQS) are coupled with their consumers. Message queues can be a good buffer to handle spiky workloads but they have a finite capacity. According to Gregor Hohpe, message queues require proper mechanisms (aka flow controls) to avoid filling the queue beyond its manageable capacity and to keep the system stable. == Ordering Guarantees in Message Queues == Amazon SQS FIFO and Azure Service Bus sessions are queue-based messaging systems that provide ordering guarantees within a message group or session attempt but do not necessarily guarantee ordered delivery in cases of retries or failures. In SQS FIFO, messages in the same message group are processed in order, with subsequent messages held until the preceding message is successfully processed or moved to the dead-letter queue (DLQ). Once a message is placed in the DLQ, it is no longer retried, creating a gap in the sequence. However, the remaining messages continue to be delivered in order. Azure Service Bus sessions function similarly by maintaining ordering within a session, provided a single consumer processes messages sequentially. The implementation differs from SQS FIFO but follows the same fundamental ordering principle. In contrast, Apache Kafka is a distributed log-based messaging system that guarantees ordering within individual partitions rather than across the entire topic. Unlike queue-based systems, Kafka retains messages in a durable, append-only log, allowing multiple consumers to read at different offsets. Kafka uses manual offset management, giving consumers control over retries and failure handling. If a consumer fails to process a message, it can delay committing the offset, preventing further progress in that partition while other partitions remain unaffected. This partition-based design enables fault isolation and parallel processing while allowing ordering to be maintained within partitions, depending on consumer handling. == Vendors == Apache Kafka Apache Kafka is a distributed system consisting of servers that store and forward messages between producer client and consumer applications. IBM MQ IBM MQ offers a managed service that can be used on IBM Cloud and Amazon Web Services. Microsoft Azure Service Bus Service Bus offers queues, topics & subscriptions, and rules/actions in order to support publish-subscribe, temporal decoupling, and load balancing scenarios. Azure Service Bus is built on AMQP allowing any existing AMQP 1.0 client stack to interact with Service Bus directly or via existing .Net, Java, Node, and Python clients. Standard and Premium tiers allow for pay as you go or isolated resources at massive scale. Oracle Messaging Cloud Service This service provides a messaging solution for applications for asynchronous communication and is influenced by the Java Message Service (JMS) API specification. Any application platform that understands HTTP can also use Oracle Messaging Cloud Service through the REST interface. For Java applications, Oracle Messaging Cloud Service provides a Java library that implements and extends the JMS 1.1 interface. The Java library implements the JMS API by acting as a client of the REST API. Amazon Simple Queue Service Supports messages natively up to 256K, or up to 2GB by transmitting payload via S3. Highly scalable, durable and resilient. Provides loose-FIFO and 'at least once' delivery in order to provide massive scale. Supports REST API and optional Java Message Service client. Low latency. Utilizes Amazon Web Services. IronMQ Supports messages up to 64k; guarantees order; guarantees once only delivery; no delays retrieving messages. Supports REST API and beanstalkd open source protocol. Runs on multiple clouds including AWS and Rackspace. Scaling must be managed by user. RabbitMQ RabbitMQ is a reliable and mature messaging and streaming broker, which is easy to deploy on cloud environments, on-premises, and on your local machine. Supports AMQP, STOMP, MQTT StormMQ Open platform supports messages up to 50Mb. Uses AMQP to avoid vendor lock-in and provide language neutrality. Locate-It Option allows customers to audit the location of their data at all times and satisfy data protection principles. AnypointMQ An enterprise multi-tenant, cloud messaging service that performs advanced asynchronous messaging scenarios between applications. Anypoint MQ is fully integrated with Anypoint Platform, offering role based access control, client application management, and connectors.

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  Strong secrecy

  Strong secrecy is a term used in formal proof-based cryptography for making propositions about the security of cryptographic protocols. It is a stronger notion of security than syntactic (or weak) secrecy. Strong secrecy is related with the concept of semantic security or indistinguishability used in the computational proof-based approach. Bruno Blanchet provides the following definition for strong secrecy: Strong secrecy means that an adversary cannot see any difference when the value of the secret changes For example, if a process encrypts a message m an attacker can differentiate between different messages, since their ciphertexts will be different. Thus m is not a strong secret. If however, probabilistic encryption were used, m would be a strong secret. The randomness incorporated into the encryption algorithm will yield different ciphertexts for the same value of m.

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  SIGINT Activity Designator

  A SIGINT Activity Designator (or SIGAD) identifies a signals intelligence (SIGINT) line of collection activity associated with a signals collection station, such as a base or a ship. For example, the SIGAD for Menwith Hill in the UK is USD1000. SIGADs are used by the signals intelligence agencies of Australia, Canada, New Zealand, the United Kingdom, and the United States (the Five Eyes). There are several thousand SIGADs including the substation SIGADs denoted with a trailing alpha character. Several dozen of these are significant. The leaked Boundless Informant reporting screenshot showed that it summarized 504 active SIGADs during a 30-day period in March 2013. == General format == A SIGAD consists of five to eight case insensitive alphanumeric characters. It takes the general form of an alphanumeric designator normally composed of a two- or three-letter prefix followed by one to three numbers. Often a dash is used to separate the alphabetic and numeric characters in the primary part of the designator, but less frequently a space is used as a separator or the alphabetic and numeric characters are concatenated together. An additional alphabetic character can be added to denote a sub-designator for a subset of the primary unit, such as a detachment. Lastly, a numeric character can be added after the aforementioned alphabetic to provide for a sub-sub-designator. In the examples below an X represents an alphabetic character and an N represents a numeric character that are part of the primary designator. Likewise, an x represents an alphabetic character and an n represents a numeric character that are part of a sub-designator. Here are valid generalized examples of SIGADs: The first two characters show which country operates the particular SIGINT facility, which can be US for the United States, UK for the United Kingdom, CA for Canada, AU for Australia and NZ for New Zealand. A third letter shows what sort of staff runs the station. SIGADs beginning with US without a third letter are used for intercept facilities run by the NSA. == PRISM SIGAD == One prominent SIGAD as of April 2013 is US-984XN, with an unclassified codename of PRISM. It is "the number one source of raw intelligence used for NSA analytic reports" according to National Security Agency sources in a document leaked by Edward Snowden. The President's Daily Brief, an all-source intelligence product, cited SIGAD US-984XN as a source in 1,477 items in 2012. The U.S. government operates the PRISM electronic surveillance collection program through NSA's Special Source Operations, an alliance with trusted telecommunications providers. == SIGADs for spy ships == The declassified SIGAD for the USS Liberty (AGTR-5) was USN-855. The USS Liberty incident occurred on 8 June 1967, during the Six-Day War, when Israeli Air Force jet fighter aircraft and Israeli Navy motor torpedo boats attacked the USS Liberty in international waters. The USS Pueblo (AGER-2) was a technical research ship, which was boarded and captured by North Korean forces on 23 January 1968, in what is known as the Pueblo incident. The declassified SIGAD for the NSA Direct Support Unit (DSU) from the Naval Security Group (NSG) on the USS Pueblo patrol involved in the incident was USN-467Y. The USS Pueblo, which officially remains a commissioned vessel of the United States Navy, is the only ship of the U.S. Navy currently being held captive. == Vietnam War SIGADs == The following are the Vietnam War-era declassified SIGADs from inside South Vietnam during the period of 1969 to 1975: Some locations have multiple SIGADs due to different types of collection activities and/or collection at different times during the period. The SIGADs beginning with USA were operated by the United States Air Force's United States Air Force Security Service (USAFSS). The SIGADs beginning with USM were operated by the United States Army's Army Security Agency (ASA). Lastly, the SIGADs beginning with USN were operated by the United States Navy's Naval Security Group (NAVSECGRU). All three of these units have been merged into other units or inactivated. The above list consists of the higher-echelon SIGADs. It does not include the numerous miscellaneous and temporary detachments, or direction finding stations belonging to major units or sites unless that detachment or site was the only one stationed in South Vietnam. Many of the "dets" were short-lived, often formed to support ongoing MACV operations or forward deployments of combat operational or maneuver units. These detachments usually were designated by a letter suffix attached to the higher-echelon SIGAD such as "USM-633J," which was a detachment of the 372d Radio Research Company, USM-633, supporting the United States Army's 25th Infantry Division. === Supporting Southeast Asia SIGADs === The following declassified SIGADs were highly relevant to the Vietnam Campaign, but were located in areas outside of South Vietnam in Southeast Asia. Again, detachments are not listed separately. In the case of the USS Maddox, naval Direct Support Units (DSUs) used the SIGAD USN-467 as a generic designator for their missions. Each specific patrol received a letter suffix for its duration. The subsequent mission would receive the next letter in an alphabetic sequence. Thus, SIGAD USN-467N specifically designates the USS Maddox patrol involved with the Gulf of Tonkin incident. == Joint Base SIGADs == In November 2005, the US Congress performed a fifth round of Base Realignment and Closure. This 2005 law also created twelve joint bases by merging adjacent installations belonging to different services in an effort to reduce costs and improve efficiencies. Joint bases with a primarily SIGINT mission have SIGADs that begin with USJ. A joint base would have a primary SIGAD in the general form of USJ-NNN, where NNN are numeric characters. An actual example is not given, since these units are currently active.

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