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  • VistaCreate

    VistaCreate

    VistaCreate (formerly Crello) is an online graphic design platform for non-designers, launched in 2016. As of 2022, it has more than 10 million users in 192 countries. == Overview == VistaCreate (then known as Crello) was launched in 2016 as a part of Depositphotos. In 2019, the product hit a milestone of 1 million registered users and also launched mobile apps. In 2020, the library of templates and objects became free. A music library and a background remover tool were added to the platform. In May 2021, Moufflons Basketball, in collaboration with VistaCreate, organized a poster design competition in support of gender equality in sports. In October 2021, Vistaprint acquired Crello and its parent company, Depositphotos, for a total price of $85 million. After the acquisition, Crello was rebranded to VistaCreate. Along with Vistaprint and 99designs, it became part of the new Vista parent brand. After Russia started a full-scale war on the territory of Ukraine in February 2022, VistaCreate suspended all business in Russia and Belarus. VistaCreate's team and Depositphotos gathered collections of images and templates dedicated to the war in Ukraine.

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  • List of cryptography journals

    List of cryptography journals

    List of cryptography journals includes notable peer-reviewed academic journals that focus on cryptography, cryptanalysis, information security, and related areas in computer science and mathematics. == Notable journals == Cryptologia Designs, Codes and Cryptography IEEE Transactions on Information Theory International Journal of Information Security Journal of Cryptology Journal of Computer Security ACM Transactions on Privacy and Security Information Processing Letters Information and Computation

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  • Cryptochannel

    Cryptochannel

    In telecommunications, a cryptochannel is a complete system of crypto-communications between two or more holders or parties. It includes: (a) the cryptographic aids prescribed; (b) the holders thereof; (c) the indicators or other means of identification; (d) the area or areas in which effective; (e) the special purpose, if any, for which provided; and (f) pertinent notes as to distribution, usage, etc. A cryptochannel is analogous to a radio circuit.

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  • Undeniable signature

    Undeniable signature

    An undeniable signature is a digital signature scheme which allows the signer to be selective to whom they allow to verify signatures. The scheme adds explicit signature repudiation, preventing a signer later refusing to verify a signature by omission; a situation that would devalue the signature in the eyes of the verifier. It was invented by David Chaum and Hans van Antwerpen in 1989. == Overview == In this scheme, a signer possessing a private key can publish a signature of a message. However, the signature reveals nothing to a recipient/verifier of the message and signature without taking part in either of two interactive protocols: Confirmation protocol, which confirms that a candidate is a valid signature of the message issued by the signer, identified by the public key. Disavowal protocol, which confirms that a candidate is not a valid signature of the message issued by the signer. The motivation for the scheme is to allow the signer to choose to whom signatures are verified. However, that the signer might claim the signature is invalid at any later point, by refusing to take part in verification, would devalue signatures to verifiers. The disavowal protocol distinguishes these cases removing the signer's plausible deniability. It is important that the confirmation and disavowal exchanges are not transferable. They achieve this by having the property of zero-knowledge; both parties can create transcripts of both confirmation and disavowal that are indistinguishable, to a third-party, of correct exchanges. The designated verifier signature scheme improves upon deniable signatures by allowing, for each signature, the interactive portion of the scheme to be offloaded onto another party, a designated verifier, reducing the burden on the signer. == Zero-knowledge protocol == The following protocol was suggested by David Chaum. A group, G, is chosen in which the discrete logarithm problem is intractable, and all operation in the scheme take place in this group. Commonly, this will be the finite cyclic group of order p contained in Z/nZ, with p being a large prime number; this group is equipped with the group operation of integer multiplication modulo n. An arbitrary primitive element (or generator), g, of G is chosen; computed powers of g then combine obeying fixed axioms. Alice generates a key pair, randomly chooses a private key, x, and then derives and publishes the public key, y = gx. === Message signing === Alice signs the message, m, by computing and publishing the signature, z = mx. === Confirmation (i.e., avowal) protocol === Bob wishes to verify the signature, z, of m by Alice under the key, y. Bob picks two random numbers: a and b, and uses them to blind the message, sending to Alice: c = magb. Alice picks a random number, q, uses it to blind, c, and then signing this using her private key, x, sending to Bob: s1 = cgq ands2 = s1x. Note that s1x = (cgq)x = (magb)xgqx = (mx)a(gx)b+q = zayb+q. Bob reveals a and b. Alice verifies that a and b are the correct blind values, then, if so, reveals q. Revealing these blinds makes the exchange zero knowledge. Bob verifies s1 = cgq, proving q has not been chosen dishonestly, and s2 = zayb+q, proving z is valid signature issued by Alice's key. Note that zayb+q = (mx)a(gx)b+q. Alice can cheat at step 2 by attempting to randomly guess s2. === Disavowal protocol === Alice wishes to convince Bob that z is not a valid signature of m under the key, gx; i.e., z ≠ mx. Alice and Bob have agreed an integer, k, which sets the computational burden on Alice and the likelihood that she should succeed by chance. Bob picks random values, s ∈ {0, 1, ..., k} and a, and sends: v1 = msga and v2 = zsya, where exponentiating by a is used to blind the sent values. Note that v2 = zsya = (mx)s(gx)a = v1x. Alice, using her private key, computes v1x and then the quotient, v1xv2−1 = (msga)x(zsgxa)−1 = msxz−s = (mxz−1)s. Thus, v1xv2−1 = 1, unless z ≠ mx. Alice then tests v1xv2−1 for equality against the values: (mxz−1)i for i ∈ {0, 1, …, k}; which are calculated by repeated multiplication of mxz−1 (rather than exponentiating for each i). If the test succeeds, Alice conjectures the relevant i to be s; otherwise, she conjectures random value. Where z = mx, (mxz−1)i = v1xv2−1 = 1 for all i, s is unrecoverable. Alice commits to i: she picks a random r and sends hash(r, i) to Bob. Bob reveals a. Alice confirms that a is the correct blind (i.e., v1 and v2 can be generated using it), then, if so, reveals r. Revealing these blinds makes the exchange zero knowledge. Bob checks hash(r, i) = hash(r, s), proving Alice knows s, hence z ≠ mx. If Alice attempts to cheat at step 3 by guessing s at random, the probability of succeeding is 1/(k + 1). So, if k = 1023 and the protocol is conducted ten times, her chances are 1 to 2100.

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  • Report generator

    Report generator

    A report generator is a computer program whose purpose is to take data from a source such as a database, XML stream or a spreadsheet, and use it to produce a document in a format which satisfies a particular human readership. Report generation functionality is almost always present in database systems, where the source of the data is the database itself. It can also be argued that report generation is part of the purpose of a spreadsheet. Standalone report generators may work with multiple data sources and export reports to different document formats. Information systems theory specifies that information delivered to a target human reader must be timely, accurate and relevant. Report generation software targets the final requirement by making sure that the information delivered is presented in the way most readily understood by the target reader. == History == An early report writer was part of NOMAD developed in the 1970s. The evolution of reporting software has a rich history dating back to the mid-20th century, driven by the increasing need for businesses to efficiently analyze and present data. Initially, manual extraction and tabulation were commonplace, but the advent of computers in the 1960s marked a transformative phase with the emergence of basic reporting tools. The 1980s saw the widespread adoption of database management systems, laying the groundwork for more sophisticated reporting capabilities. Notable dedicated reporting software, such as Crystal Reports and BusinessObjects, gained prominence in the 1990s amidst the growing demand for business intelligence. The 21st century witnessed a paradigm shift towards web-based reporting solutions and the rise of self-service BI tools, empowering users to create reports independently. Presently, reporting software continues to evolve with a focus on data visualization, integration of artificial intelligence, and the imperative for real-time analytics in decision-making.

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  • Torus interconnect

    Torus interconnect

    A torus interconnect is a switch-less network topology for connecting processing nodes in a parallel computer system. == Introduction == In geometry, a torus is created by revolving a circle about an axis coplanar to the circle. While this is a general definition in geometry, the topological properties of this type of shape describes the network topology in its essence. === Geometry illustration === In the representations below, the first is a one dimension torus, a simple circle. The second is a two dimension torus, in the shape of a 'doughnut'. The animation illustrates how a two dimension torus is generated from a rectangle by connecting its two pairs of opposite edges. At one dimension, a torus topology is equivalent to a ring interconnect network, in the shape of a circle. At two dimensions, it becomes equivalent to a two dimension mesh, but with extra connection at the edge nodes. === Torus network topology === A torus interconnect is a switch-less topology that can be seen as a mesh interconnect with nodes arranged in a rectilinear array of N = 2, 3, or more dimensions, with processors connected to their nearest neighbors, and corresponding processors on opposite edges of the array connected.[1] In this lattice, each node has 2N connections. This topology is named for the lattice formed in this way, which is topologically homogeneous to an N-dimensional torus. == Visualization == The first 3 dimensions of torus network topology are easier to visualize and are described below: 1D Torus: one dimension, n nodes are connected in closed loop with each node connected to its two nearest neighbors. Communication can take place in two directions, +x and −x. A 1D Torus is the same as ring interconnection. 2D Torus: two dimensions with degree of four, the nodes are imagined laid out in a two-dimensional rectangular lattice of n rows and n columns, with each node connected to its four nearest neighbors, and corresponding nodes on opposite edges connected. Communication can take place in four directions, +x, −x, +y, and −y. The total nodes of a 2D Torus is n2. 3D Torus: three dimensions, the nodes are imagined in a three-dimensional lattice in the shape of a rectangular prism, with each node connected with its six neighbors, with corresponding nodes on opposing faces of the array connected. Each edge consists of n nodes. communication can take place in six directions, +x, −x, +y, −y, +z, −z. Each edge of a 3D Torus consist of n nodes. The total nodes of 3D Torus is n3. ND Torus: N dimensions, each node of an N dimension torus has 2N neighbors, Communication can take place in 2N directions. Each edge consists of n nodes. Total nodes of this torus is nN. The main motivation of having higher dimension of torus is to achieve higher bandwidth, lower latency, and higher scalability. Higher-dimensional arrays are difficult to visualize. The above ruleset shows that each higher dimension adds another pair of nearest neighbor connections to each node. == Performance == A number of supercomputers on the TOP500 list use three-dimensional torus networks, e.g. IBM's Blue Gene/L and Blue Gene/P, and the Cray XT3. IBM's Blue Gene/Q uses a five-dimensional torus network. Fujitsu's K computer and the PRIMEHPC FX10 use a proprietary three-dimensional torus 3D mesh interconnect called Tofu. === 3D Torus performance simulation === Sandeep Palur and Dr. Ioan Raicu from Illinois Institute of Technology conducted experiments to simulate 3D torus performance. Their experiments ran on a computer with 250GB RAM, 48 cores and x86_64 architecture. The simulator they used was ROSS (Rensselaer’s Optimistic Simulation System). They mainly focused on three aspects: Varying network size Varying number of servers Varying message size They concluded that throughput decreases with the increase of servers and network size. Otherwise, throughput increases with the increase of message size. === 6D Torus product performance === Fujitsu Limited developed a 6D torus computer model called "Tofu". In their model, a 6D torus can achieve 100 GB/s off-chip bandwidth, 12 times higher scalability than a 3D torus, and high fault tolerance. The model is used in the K computer and Fugaku. === Cost === While long wrap-around links may be the easiest way to visualize the connection topology, in practice, restrictions on cable lengths often make long wrap-around links impractical. Instead, directly connected nodes—including nodes that the above visualization places on opposite edges of a grid, connected by a long wrap-around link—are physically placed nearly adjacent to each other in a folded torus network. Every link in the folded torus network is very short—almost as short as the nearest-neighbor links in a simple grid interconnect—and therefore low-latency.

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  • Data definition specification

    Data definition specification

    In computing, a data definition specification (DDS) is a guideline to ensure comprehensive and consistent data definition. It represents the attributes required to quantify data definition. A comprehensive data definition specification encompasses enterprise data, the hierarchy of data management, prescribed guidance enforcement and criteria to determine compliance. == Overview == A data definition specification may be developed for any organization or specialized field, improving the quality of its products through consistency and transparency. It eliminates redundancy (since all contributing areas are referencing the same specification) and provides standardization and degrees of compliance, making it easier and more efficient to create, modify, verify, analyze and share information across the enterprise. To understand how a data definition specification works in an enterprise, we must look at the elements of a DDS. Writing data definitions, defining business terms (or rules) in the context of a particular environment, provides structure for an organization's data architecture. In developing these definitions, the words used must be traceable to clearly defined data. A data definition specification may be used in the following activities: Business intelligence Business process modeling Business rules management Data analysis and modeling Information architecture Metadata modeling Data mastering Report generation == Criteria == A data definition specification requires data definitions to be: Atomic – singular, describing only one concept. Commonly used and ambiguous terms should be defined. While a term refers to one concept, several words may be used in a term: File – A concept identifiable with one word File extension – A concept identifiable with more than one word Traceable – Mapped to a specific data element. In business, a term may be traced to an entity (for example, a customer) or an attribute (such as a customer's name). A term may be a value in a data set (such as gender), or designate the data set itself. Traceability indicates relationships in the data hierarchy. Consistent - Used in a standard syntax; if used in a specific context, the context is noted Accurate - Precise, correct and unambiguous, stating what the term is and is not Clear - Readily understood by the reader Complete - With the term, its description and contextual references Concise - To avoid circular references == Applications == === Enterprise data === A data definition specification was produced by the Open Mobile Alliance to document charging data. The document, the centralized catalog of data elements defined for interfaces, specifies the mapping of these data elements to protocol fields in the interfaces. Created for the exchange of financial data, Market Data Definition Language (MDDL) is an XML specification designed to enable the interchange of information necessary to account, to analyze, and to trade financial instruments of the world's markets. It defines an XML-based interchange format and common data dictionary on the fields needed to describe: (1) financial instruments, (2) corporate events affecting value and tradability, and (3) market-related, economic and industrial indicators. The principal function of MDDL is to allow entities to exchange market data by standardizing formats and definitions. MDDL provides a common format for market data so that it can be efficiently passed from one processing system to another and provides a common understanding of market data content by standardizing terminology and by normalizing the relationships of various data elements to one another ... From the user perspective, the goal of MDDL is to enable users to integrate data from multiple sources by standardizing both the input feeds used for data warehousing (i.e., define what's being provided by vendors) and the output methods by which client applications request the data (i.e., ensure compatibility on how to get data in and out of applications)." === Clinical submissions === The Clinical Data Interchange Standards Consortium, a global, multidisciplinary, non-profit organization, has established standards to support the acquisition, exchange, submission and archiving of clinical research data and metadata. CDISC standards are vendor-neutral, platform-independent and freely available from the CDISC website. The Case Report Tabulation Data Definition Specification (define.xml) draft version 2.0, the oldest data definition specification, is part of the evolution from the 1999 FDA electronic submission (eSub) guidance and electronic Common Technical Document (eCTD) documents specifying that a document describing the content and structure of included data be included in a submission. Define.xml was developed to automate the review process by generating a machine-readable data-definition document. Define.xml has standardized submissions to the Food and Drug Administration, reducing review times from over two years to several months. === Archival data === A data definition specification is the foundation of metadata for scientific data archiving. The Metadata Encoding and Transmission Standard (METS) uses one principle of a DDS: consistent use of key terms to catalog digital objects for global use. The METS schema is a flexible mechanism for encoding descriptive, administrative and structural metadata for a digital library object and expressing complex links between metadata, and can provide a useful standard for the exchange of digital-library objects between repositories. A similar effort is underway to preserve complex data associated with video-game archiving. Preserving Virtual Worlds attempted to address archival-format deficiencies, citing the lack of suitable documentation for interactive fiction and games at the bit level: specifically, the absence of "representation information" needed to map raw bits into higher-level data constructs. Preserving Virtual Worlds 2 is a research project expanding on initial efforts in this field.

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  • Professional network service

    Professional network service

    A professional network service (or, in an Internet context, simply a professional network) is a type of social network service that focuses on interactions and relationships for business opportunities and career growth, with less emphasis on activities in personal life. A professional network service is used by working individuals, job-seekers, and businesses to establish and maintain professional contacts, to find work or hire employees, share professional achievements, sell or promote services, and stay up-to-date with industry news and trends. According to LinkedIn managing director Clifford Rosenberg in an interview with AAP in 2010, "[t]his is a call to action for professionals to re-address their use of social networks and begin to reap as many rewards from networking professionally as they do personally." Businesses mostly depend on resources and information outside the company and to get what they need, they need to reach out and professionally network with others, such as employees or clients as well as potential opportunities. "Nardi, Whittaker, and Schwarz (2002) point out three main tasks that they believe networkers need to attend to keep a successful professional (intentional) network: building a network, maintaining the network, and activating selected contacts. They stress that networkers need to continue to add new contacts to their network to access as many resources as possible and to maintain their network by staying in touch with their contacts. This is so that the contacts are easy to activate when the networker has work that needs to be done." By using a professional network service, businesses can keep all of their networks up-to-date, and in order, and helps figure out the best way to efficiently get in touch with each of them. A service that can do all that helps relieve some of the stress when trying to get things done. Not all professional network services are online sites that help promote a business. Some services connect the user to other services that help promote the business other than online sites, such as phone/Internet companies that provide services and companies that specifically are designed to do all of the promoting, online and in person, for a business. == History == In 1997, professional network services started up throughout the world and continue to grow. The first recognizable site to combine all features, such as creating profiles, adding friends, and searching for friends, was SixDegrees.com. According to Boyd and Ellison's article, "Social Network Sites: Definition, History, and Scholarship", from 1997 to 2001, several community tools began supporting various combinations of profiles and publicly articulated Friends. Boyd and Ellison go on to say that the next wave began with Ryze.com in 2001. It was introduced as a new way "to help people leverage their business networks". == Inside the works == Quite a lot of work is put into a professional network service, such as the number of hours that go into them and the type of people they work for, as well as the business model of it all, such as the professional interaction and the multiple services they deal with. === Types of services === Some professional network services not only help promote the business but can also help in connecting to other people. Those services may include a specific phone and/or Internet company or a company that helps to connect with other businesses. According to the Society for New Communications Research (SNCR), there are at least nine online professional networks that are being used. === Professional interaction === Kaplan and Haenlein elaborate on five key considerations for companies when utilizing media. These include the importance of careful selection, the option to choose existing applications or develop custom ones, ensuring alignment with organizational activities, integrating a comprehensive media plan, and providing accessibility to all stakeholders. ==== Choose carefully ==== "Choosing the right medium for any given purpose depends on the target group to be reached and the message to be communicated. On one hand, each Social Media application usually attracts a certain group of people, and firms should be active wherever their customers are present. On the other hand, there may be situations whereby certain features are necessary to ensure effective communication, and these features are only offered by one specific application." ==== Ensure activity alignment ==== "Sometimes you may decide to rely on various Social Media, or a set of different applications within the same group, to have the largest possible reach." "Using different contact channels can be a worthwhile and profitable strategy." According to the Society for New Communications Research at Harvard University, "the average professional belongs to 3–5 online networks for business use, and LinkedIn, Facebook, and Twitter are among the top used." ==== Integrate a media plan ==== Social media and traditional media are "both part of the same: your corporate image" in the customers' eyes. ==== Allow access to all ==== "...once the firm has decided to utilize Social Media applications, it is worth checking that all employees may access them." According to the SNCR, "the convergence of Internet, mobile, and social media has taken significant shape as professionals rely on anywhere access to information, relationships, and networks." ==== Online usage ==== "Half of the respondents report participating in 3 to 5 online professional networks. Another three in ten participate in 6 or more professional networks." "Popular social networks are now being used frequently as Professional Communities. More than nine in ten respondents indicated that they use LinkedIn and half reported using Facebook. Twitter and blogs were frequently listed as 'professional networks'." === Business model === According to Michael Rappa's article, Business models on the Web", "a business model is the method of doing business by which a company can sustain itself – that is, generate revenue. The business model spells out how a company makes money by specifying where it is positioned in the value chain." Rappa mentions that there are at least nine basic categories from which a business model can be separated. Those categories are a brokerage, advertising, infomediary, merchant, manufacturer, affiliate, community, subscription, and utility. "...a firm may combine several different models as part of its overall Internet business strategy." At first, Flickr started as a way to mainstream public relations. == Social impact == When it comes to the social impact that professional network services have on today's society, it has proved to increase activity. According to the SNCR, "[t]hree quarters of respondents rely on professional networks to support business decisions. Reliance has increased for essentially all respondents over the past three years. Younger (20–35) and older professionals (55+) are more active users of social tools than middle-aged professionals. More people are collaborating outside their company wall than within their organizational intranet." == Limitations == Since the internet and social media are a part of this "world where consumers can speak so freely with each other and businesses have increasingly less control over the information available about them in cyberspace", most firms and businesses are uncomfortable with all the freedom. According to Kaplan and Haenlein's article, "Users of the world, unite! The challenges and opportunities of Social Media", businesses are pushed aside and are only able to sit back and watch as their customers publicly post comments, which may or may not be well-written.

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  • Suno (platform)

    Suno (platform)

    Suno is a generative artificial intelligence music creation platform. It is designed to generate music that can include vocals and instrumentation. The platform was initially developed by Suno, Inc., of Cambridge, Massachusetts. Suno has been widely available since December 20, 2023, after the launch of a web application and a partnership with Microsoft, which included Suno as a plugin in Microsoft Copilot. The program operates by producing songs based on text or audio prompts provided by its users. Suno does not disclose the dataset used to train its artificial intelligence. == History == Suno, Inc., was founded by four people: Michael Shulman, Georg Kucsko, Martin Camacho, and Keenan Freyberg. They all worked for Kensho, an AI startup, before starting their own company in Cambridge, Massachusetts. In April 2023, Suno released their open-source text-to-speech and audio model called "Bark" on GitHub. On March 21, 2024, Suno released its V3 version for all users. The new version allowed users to create a limited number of four-minute songs using a free account. Users can pay for more features. In April 2024, a sentimental ballad was generated with Suno based on the text of the MIT License. In June 2024, a lawsuit, led by the Recording Industry Association of America, was filed against Suno and Udio alleging widespread infringement of copyrighted sound recordings. The lawsuit sought to bar the companies from training on copyrighted music, as well as damages of up to $150,000 per work from infringements that have already taken place. On July 1, 2024, a mobile app for Suno was released. On November 19, 2024, Suno upgraded its AI song model program to v4. In January 2025, Michael Shulman remarked on a podcast, "I think the majority of people don't enjoy the majority of the time they spend making music." In March 2025, one day after thousands of musicians including Thom Yorke and ABBA's Björn Ulvaeus signed a letter calling for Suno to stop training its model on copyrighted music, Timbaland endorsed Suno in a video on the company's website. In July 2025, Suno user imoliver signed a record deal with Hallwood Media, which became the first instance of a traditional music label signing an AI-based creator. Hallwood later signed with AI-artist Xania Monet for US$3 million. Monet's songs were generated by Suno AI by poet Telisha Jones. In November 2025, Suno agreed to a $500 million dollar lawsuit settlement, in which Suno would be allowed to train its models on Warner Music Group's music catalog, and WMG would control aspects of AI likeness, music, audio, software, copyrights, AI tools and music created by users on Suno. As part of the settlement, Suno also acquired the concert discovery platform Songkick from WMG. == Controversy == Suno, Inc., has been sued by the Recording Industry Association of America for copyright infringement, and thousands of musicians have signed a letter demanding that the company cease using copyrighted music in their training data. Suno does not disclose the dataset used to train its artificial intelligence.

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  • Tokenization (data security)

    Tokenization (data security)

    Tokenization, when applied to data security, is the process of substituting a sensitive data element with a non-sensitive equivalent, referred to as a token, that has no intrinsic or exploitable meaning or value. The token is a reference (i.e. identifier) that maps back to the sensitive data through a tokenization system. The mapping from original data to a token uses methods that render tokens infeasible to reverse in the absence of the tokenization system, for example using tokens created from random numbers. A one-way cryptographic function is used to convert the original data into tokens, making it difficult to recreate the original data without obtaining entry to the tokenization system's resources. To deliver such services, the system maintains a vault database of tokens that are connected to the corresponding sensitive data. Protecting the system vault is vital to the system, and improved processes must be put in place to offer database integrity and physical security. The tokenization system must be secured and validated using security best practices applicable to sensitive data protection, secure storage, audit, authentication and authorization. The tokenization system provides data processing applications with the authority and interfaces to request tokens, or detokenize back to sensitive data. The security and risk reduction benefits of tokenization require that the tokenization system is logically isolated and segmented from data processing systems and applications that previously processed or stored sensitive data replaced by tokens. Only the tokenization system can tokenize data to create tokens, or detokenize back to redeem sensitive data under strict security controls. The token generation method must be proven to have the property that there is no feasible means through direct attack, cryptanalysis, side channel analysis, token mapping table exposure or brute force techniques to reverse tokens back to live data. Replacing live data with tokens in systems is intended to minimize exposure of sensitive data to those applications, stores, people and processes, reducing risk of compromise or accidental exposure and unauthorized access to sensitive data. Applications can operate using tokens instead of live data, with the exception of a small number of trusted applications explicitly permitted to detokenize when strictly necessary for an approved business purpose. Tokenization systems may be operated in-house within a secure isolated segment of the data center, or as a service from a secure service provider. Tokenization may be used to safeguard sensitive data involving, for example, bank accounts, financial statements, medical records, criminal records, driver's licenses, loan applications, stock trades, voter registrations, and other types of personally identifiable information (PII). Tokenization is often used in credit card processing. The PCI Council defines tokenization as "a process by which the primary account number (PAN) is replaced with a surrogate value called a token. A PAN may be linked to a reference number through the tokenization process. In this case, the merchant simply has to retain the token and a reliable third party controls the relationship and holds the PAN. The token may be created independently of the PAN, or the PAN can be used as part of the data input to the tokenization technique. The communication between the merchant and the third-party supplier must be secure to prevent an attacker from intercepting to gain the PAN and the token. De-tokenization is the reverse process of redeeming a token for its associated PAN value. The security of an individual token relies predominantly on the infeasibility of determining the original PAN knowing only the surrogate value". The choice of tokenization as an alternative to other techniques such as encryption will depend on varying regulatory requirements, interpretation, and acceptance by respective auditing or assessment entities. This is in addition to any technical, architectural or operational constraint that tokenization imposes in practical use. == Concepts and origins == The concept of tokenization, as adopted by the industry today, has existed since the first currency systems emerged centuries ago as a means to reduce risk in handling high value financial instruments by replacing them with surrogate equivalents. In the physical world, coin tokens have a long history of use replacing the financial instrument of minted coins and banknotes. In more recent history, subway tokens and casino chips found adoption for their respective systems to replace physical currency and cash handling risks such as theft. Exonumia and scrip are terms synonymous with such tokens. In the digital world, similar substitution techniques have been used since the 1970s as a means to isolate real data elements from exposure to other data systems. In databases for example, surrogate key values have been used since 1976 to isolate data associated with the internal mechanisms of databases and their external equivalents for a variety of uses in data processing. More recently, these concepts have been extended to consider this isolation tactic to provide a security mechanism for the purposes of data protection. In the payment card industry, tokenization is one means of protecting sensitive cardholder data in order to comply with industry standards and government regulations. Tokenization was applied to payment card data by Shift4 Corporation and released to the public during an industry Security Summit in Las Vegas, Nevada in 2005. The technology is meant to prevent the theft of the credit card information in storage. Shift4 defines tokenization as: "The concept of using a non-decryptable piece of data to represent, by reference, sensitive or secret data. In payment card industry (PCI) context, tokens are used to reference cardholder data that is managed in a tokenization system, application or off-site secure facility." To protect data over its full lifecycle, tokenization is often combined with end-to-end encryption to secure data in transit to the tokenization system or service, with a token replacing the original data on return. For example, to avoid the risks of malware stealing data from low-trust systems such as point of sale (POS) systems, as in the Target breach of 2013, cardholder data encryption must take place prior to card data entering the POS and not after. Encryption takes place within the confines of a security hardened and validated card reading device and data remains encrypted until received by the processing host, an approach pioneered by Heartland Payment Systems as a means to secure payment data from advanced threats, now widely adopted by industry payment processing companies and technology companies. The PCI Council has also specified end-to-end encryption (certified point-to-point encryption—P2PE) for various service implementations in various PCI Council Point-to-point Encryption documents. == The tokenization process == The process of tokenization consists of the following steps: The application sends the tokenization data and authentication information to the tokenization system. It is stopped if authentication fails and the data is delivered to an event management system. As a result, administrators can discover problems and effectively manage the system. The system moves on to the next phase if authentication is successful. Using one-way cryptographic or random generation techniques, a token is generated and kept in a highly secure data vault. The new token is provided to the application for further use, replacing the sensitive data for processing and storage. Tokenization systems share several components according to established standards. Token generation is the process of producing a token using any means, such as one-way nonreversible cryptographic functions (e.g., a hash function with a strong, secret salt) or assignment via a randomly generated number. Random number generator (RNG) techniques are often the best choice for generating token values. Token mapping – this is the process of assigning the created token value to its original value. To enable permitted look-ups of the original value using the token as the index, a secure cross-reference database must be constructed. Token data store – this is a central repository for the token mapping process that holds the original sensitive values and their related token values. Sensitive data and token values must be securely kept in an encrypted format. Management of cryptographic keys. Strong key management procedures are required for sensitive data encryption on token data stores. == Difference from encryption == Tokenization and "classic" encryption effectively protect data if implemented properly, and a computer security system may use both. While similar in certain regards, tokenization and classic encryption differ in a few key aspects. Both are cryptographic data security methods and the

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  • Yao's test

    Yao's test

    In cryptography and the theory of computation, Yao's test is a test defined by Andrew Chi-Chih Yao in 1982, against pseudo-random sequences. A sequence of words passes Yao's test if an attacker with reasonable computational power cannot distinguish it from a sequence generated uniformly at random. == Formal statement == === Boolean circuits === Let P {\displaystyle P} be a polynomial, and S = { S k } k {\displaystyle S=\{S_{k}\}_{k}} be a collection of sets S k {\displaystyle S_{k}} of P ( k ) {\displaystyle P(k)} -bit long sequences, and for each k {\displaystyle k} , let μ k {\displaystyle \mu _{k}} be a probability distribution on S k {\displaystyle S_{k}} , and P C {\displaystyle P_{C}} be a polynomial. A predicting collection C = { C k } {\displaystyle C=\{C_{k}\}} is a collection of boolean circuits of size less than P C ( k ) {\displaystyle P_{C}(k)} . Let p k , S C {\displaystyle p_{k,S}^{C}} be the probability that on input s {\displaystyle s} , a string randomly selected in S k {\displaystyle S_{k}} with probability μ ( s ) {\displaystyle \mu (s)} , C k ( s ) = 1 {\displaystyle C_{k}(s)=1} , i.e. Moreover, let p k , U C {\displaystyle p_{k,U}^{C}} be the probability that C k ( s ) = 1 {\displaystyle C_{k}(s)=1} on input s {\displaystyle s} a P ( k ) {\displaystyle P(k)} -bit long sequence selected uniformly at random in { 0 , 1 } P ( k ) {\displaystyle \{0,1\}^{P(k)}} . We say that S {\displaystyle S} passes Yao's test if for all predicting collection C {\displaystyle C} , for all but finitely many k {\displaystyle k} , for all polynomial Q {\displaystyle Q} : === Probabilistic formulation === As in the case of the next-bit test, the predicting collection used in the above definition can be replaced by a probabilistic Turing machine, working in polynomial time. This also yields a strictly stronger definition of Yao's test (see Adleman's theorem). Indeed, one could decide undecidable properties of the pseudo-random sequence with the non-uniform circuits described above, whereas BPP machines can always be simulated by exponential-time deterministic Turing machines.

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  • Social knowledge management

    Social knowledge management

    Social knowledge management is a business approach that aims to leverage the collective intelligence and social interactions of an organization’s members and stakeholders. It is a branch of knowledge management, which is a multidisciplinary field that deals with the creation, sharing, and use of knowledge in various domains, such as business, economics, psychology, and information management. Knowledge management seeks to enhance organizational performance, innovation, and competitiveness by managing the intangible assets of an organization, such as human capital, know-how, technology, customers, and networks. Social media plays a crucial role in social knowledge management by enhancing communication, collaboration, and learning among individuals and groups, both internally and externally. It offers valuable insights and feedback from customers, partners, and stakeholders, and aids in generating and disseminating new knowledge. In a business context, social media is utilized for various purposes, including sentiment analysis, social learning, social collaboration, and social knowledge management. Social knowledge management is one of the application areas of social media in a business context next to others like sentiment analysis, social learning or social collaboration. Social media use by businesses can strive to achieve the following things from social media strategy point of view: learn, listen, engage in conversation, measure and refine, develop capabilities, define activities, prioritize objectives etc. Social media are not only transforming private communication and interaction, they also will transform how people work. With social media knowledge work in organizations can be optimized extremely: like a better distribution sharing and access to knowledge. This will be more and more important, as in today's business world, speed and complexity increase dramatically, while work environments change constantly. == Examples of Social KM platforms == Elium, a European software application which combines social tagging, bookmarking and networking paradigms to address internal information management purposes. Sciomino was a startup enterprise social network for Social Knowledge Management.

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  • Enonic XP

    Enonic XP

    Enonic XP is a free and open-source content platform. Developed by the Norwegian software company Enonic, the platform can be used to build websites, progressive web applications, or web-based APIs. Enonic XP uses an application framework for coding server logic with JavaScript, and has no need for SQL as it ships with an integrated content repository. The CMS is fully decoupled, meaning developers can create traditional websites and landing pages, or use XP in headless mode, that is without the presentation layer, for loading editorial content onto any device or client. Enonic is used by major organizations in Norway, including the national postal service Norway Post, the insurance company Gjensidige, the Norwegian Labour and Welfare Administration, and all the top football clubs in the national football league for men, Eliteserien. == Overview == Enonic XP ships with the content management system (CMS) Content Studio. This includes a visual drag and drop editor, a landing page editor, support for multi-site and multi-language, media and structured content, advanced image editing, responsive user interface, permissions and roles management, revision and version control, and bulk publishing. Integrations and applications can be directly installed via the "Applications" section in XP, where the platform finds apps approved in the official Enonic Market. There are no third-party databases in Enonic XP. Instead, the developers have built a distributed storage repository, avoiding the need to index content. The system brings together capabilities from Filesystem, NoSQL, document stores, and search in the storage technology, which automatically indexes everything put into the storage. Enonic XP supports deployment of server side JavaScript. The open-source framework runs on top of a JVM (Java virtual machine), and allows developers to run the same code in the browser and on the server, thus enabling them to employ JavaScript. While running on the Java virtual machine, Enonic XP can be deployed on most infrastructures. The dependency on a third-party application server to deploy code has been removed, as the platform is an application server by default. A developer can for instance insert his own modules and code straight into the system while it is running. JavaScript unifies all the technical elements, and Enonic XP features a MVC framework where everything on the back-end can be coded with server-side JavaScript. The Enonic platform can use any template engine. === Progressive web apps === Another feature of Enonic XP is the possibility for developers to create progressive web apps (PWA). A PWA is a web application that is a regular web page or website, but can appear to the user like a mobile application. === Headless CMS and integrations === Enonic XP is headless, which means it separates content and presentation. The platform supports GraphQL, provides several default APIs, and allows for building custom APIs through the Guillotine starter kit. Consequently, Enonic supports modern front-end frameworks, and offers integrations with e.g. Next.js and React. == History == Enonic AS was founded in 2000 by Morten Øien Eriksen and Thomas Sigdestad. The software company specialized in building services and solutions, including a content management system known as "Vertical Site", then "Enonic CMS". Being aware that they had application, database, and website teams working on separate silos toward the same goal, Enonic sought to combine the different elements into a single software. The resulting application platform Enonic XP, first released in 2015, includes a CMS as an optional surface layer. In March 2020, Enonic XP was ranked by SoftwareReviews, a division of Info-Tech Research Group, a Canadian IT research and analyst firm, as the "Leader" in Web Experience Management. The ranking is based on user reviews, and is featured in SoftwareReviews‘ Digital Experience Data Quadrant Report, a comprehensive evaluation and ranking of leading Web Experience Management vendors. Enonic was also ranked first in 2021 and 2022. === Release history === Enonic XP assumed the mantle from the previous content management system Enonic CMS, and thus began with "version 5.0.0." The following list only contains major releases. == Development and support == Enonic offers a user and developer community consisting of a forum, support system with tickets, documentation, codex, learning and training center with certifications, and various community groups. Writing about the support system, Mike Johnston of CMS Critic notes that "enterprise customers obviously get access to a higher level of personalized support, where the Enonic support team can respond as fast as two hours." The support system is divided in three levels: silver, gold and platinum—from next day business support to 24/7 support. As Enonic XP is open-source, known vulnerabilities, bugs and issues are listed on GitHub.

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  • Brooklyn Bridge (software)

    Brooklyn Bridge (software)

    The Brooklyn Bridge from White Crane Systems was a data transfer enabler. Although it came with some hardware, it was the software which was the basis of the product. It also could transform the data's format. == Overview == The New York Times described its category as being among "communications packages used to transfer files." In an era of 300 baud, Brooklyn Bridge operated at "115,200 baud" so that a transfer which "at 300 baud took 4 minutes and 36 seconds" only needed 5 seconds. Unlike some communications packages, this one retains the original version-date, so as not to alarm people when they seem to have what looks like an update, when it's not. == Description == Once the software is installed, users comfortable with typing the word "COPY" can do so as readily as they sneakernet. An earlier review described it as "less cumbersome than conventional communications software" The use of neither specialized hardware nor specialized software is ideal in an era when this can be done using online or other "outside" services.

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  • Software token

    Software token

    A software token (a.k.a. soft token) is a piece of a two-factor authentication security device that may be used to authorize the use of computer services. Software tokens are stored on a general-purpose electronic device such as a desktop computer, laptop, PDA, or mobile phone and can be duplicated. (Contrast hardware tokens, where the credentials are stored on a dedicated hardware device and therefore cannot be duplicated — absent physical invasion of the device) Because software tokens are something one does not physically possess, they are exposed to unique threats based on duplication of the underlying cryptographic material - for example, computer viruses and software attacks. Both hardware and software tokens are vulnerable to bot-based man-in-the-middle attacks, or to simple phishing attacks in which the one-time password provided by the token is solicited, and then supplied to the genuine website in a timely manner. Software tokens do have benefits: there is no physical token to carry, they do not contain batteries that will run out, and they are cheaper than hardware tokens. == Security architecture == There are two primary architectures for software tokens: shared secret and public-key cryptography. For a shared secret, an administrator will typically generate a configuration file for each end-user. The file will contain a username, a personal identification number, and the secret. This configuration file is given to the user. The shared secret architecture is potentially vulnerable in a number of areas. The configuration file can be compromised if it is stolen and the token is copied. With time-based software tokens, it is possible to borrow an individual's PDA or laptop, set the clock forward, and generate codes that will be valid in the future. Any software token that uses shared secrets and stores the PIN alongside the shared secret in a software client can be stolen and subjected to offline attacks. Shared secret tokens can be difficult to distribute, since each token is essentially a different piece of software. Each user must receive a copy of the secret, which can create time constraints. Some newer software tokens rely on public-key cryptography, or asymmetric cryptography. This architecture eliminates some of the traditional weaknesses of software tokens, but does not affect their primary weakness (ability to duplicate). A PIN can be stored on a remote authentication server instead of with the token client, making a stolen software token no good unless the PIN is known as well. However, in the case of a virus infection, the cryptographic material can be duplicated and then the PIN can be captured (via keylogging or similar) the next time the user authenticates. If there are attempts made to guess the PIN, it can be detected and logged on the authentication server, which can disable the token. Using asymmetric cryptography also simplifies implementation, since the token client can generate its own key pair and exchange public keys with the server.

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