Verge3D is a real-time renderer and a toolkit used for creating interactive 3D experiences running on websites. == Overview == Verge3D enables users to convert content from 3D modelling tools (Blender, 3ds Max, and Maya are currently supported) to view in a web browser. Verge3D was created by the same core group of software engineers that previously created the Blend4Web framework. == Features == Verge3D uses WebGL for rendering. It incorporates components of the Three.js library and exposes its API to application developers. Puzzles Application functionality can be added via JavaScript, either by writing code directly or by using Puzzles, Verge3D’s visual programming environment based on Google Blockly. Puzzles is aimed primarily at non-programmers allowing quick creation of interactive scenarios in a drag-and-drop fashion. App Manager and web publishing App Manager is a lightweight web-based tool for creating, managing and publishing Verge3D projects, running on top of the local development server. Verge3D Network service integrated in the App Manager allows for publishing Verge3D applications via Amazon S3 and EC2 cloud services. PBR For purposes of authoring materials, a glTF 2.0-compliant physically based rendering pipeline is offered alongside the standard shader-based approach. PBR textures can be authored using external texturing software such as Substance Painter for which Verge3D offers the corresponding export preset. Besides the glTF 2.0 model, Verge3D supports physical materials of 3ds Max and Maya (with Autodesk Arnold as reference), and Blender's real-time Eevee materials. glTF and DCC software integration Verge3D integrates directly with Blender, 3ds Max, and Maya, enabling users to create 3D geometry, materials, and animations inside the software, then export them in the JSON-based glTF format. The Sneak Peek feature allows for exporting and viewing scenes from the DCC tool environment. Facebook 3D posts For Facebook publishing, Verge3D offers a specific GLB export option. The exported GLB files are displayed and can be opened in the App Manager. Asset compression Exported files can optionally use LZMA compression, resulting in a reduction in file size of up to 6x. UI and website layouts Interface layouts, created using external WYSIWYG editors, can be linked with Puzzles to trigger changes to a 3D scene being rendered in the browser and vice versa. Animation Verge3D supports skeletal animation, including animation of bipeds and character rigs, and allows for animation of material parameters. Model parts can also be set up to be dragged by the user. Physics The physics module can be linked separately to enable collision detection, dynamically moving objects, support for characters and vehicles, springs, ropes and cloth simulation. As of version 2.11, simple physics simulations can be created and controlled without coding via Puzzles, the visual programming system used by Verge3D. AR/VR The 2.10 update added support for WebXR, an in-development open technology designed to enable virtual reality and augmented reality experiences to be displayed in web browsers. It works with both headsets with controllers, like the HTC Vive and Oculus Rift, and those without, like Google Cardboard. AR/VR experiences can enabled via Puzzles or JavaScript. == Workflow == Verge3D's workflow differs substantially from other mainstream WebGL frameworks. Development of a new Verge3D application is usually started from modeling, texturing and animating 3D objects. The models are assembled in the 3D authoring tool. The scene file is then used as a basis for a Verge3D project initialized from the App Manager. An interactive scenario is optionally added using the Puzzles editor. A Verge3D application can be previewed in the web browser at any development stage using the App Manager. The finished web application can be deployed on the Verge3D Network, on Facebook or on the user's website. == Notable uses == NASA's Jet Propulsion Laboratory used Verge3D to create an interactive 3D visualization of the Mars InSight lander. The web application allows for exploring and interacting with the real-time model of the spacecraft, with the possibility to move different parts and unfurl the solar panels. NASA's older interactive web application Experience Curiosity was ported to Verge3D from Blend4Web. The application makes it possible to operate the rover, control its cameras and the robotic arm and reproduces some of the prominent events of the Mars Science Laboratory mission. Route 66 Digital's Escape Room used Verge3D and Blender. This interactive short explores how users can navigate 3D spaces and interact with objects without the need for instruction.
Perceptual robotics
Perceptual robotics is an interdisciplinary science linking Robotics and Neuroscience. It investigates biologically motivated robot control strategies, concentrating on perceptual rather than cognitive processes and thereby sides with J. J. Gibson's view against the Poverty of the stimulus theory. As a working definition, the following quote from Chapter 64 by H. Bülthoff, C. Wallraven and M. Giese from The Springer Handbook of Robotics, edited by Bruno Siciliano and Oussama Khatib, published by Springer in 2007, could be used: In the following we will apply the term Perceptual Robotics to signify the design of robots based on principles that are derived from human perception on all three levels in the sense of Marr. This includes a realization in terms of specific neural circuits as well as the transfer of more abstract biologically-inspired strategies for the solution of relevant computational problems.
Initialization vector
In cryptography, an initialization vector (IV) or starting variable is an input to a cryptographic primitive being used to provide the initial state. The IV is typically required to be random or pseudorandom, but sometimes an IV only needs to be unpredictable or unique. Randomization is crucial for some encryption schemes to achieve semantic security, a property whereby repeated usage of the scheme under the same key does not allow an attacker to infer relationships between (potentially similar) segments of the encrypted message. For block ciphers, the use of an IV is described by the modes of operation. Some cryptographic primitives require the IV only to be non-repeating, and the required randomness is derived internally. In this case, the IV is commonly called a nonce (a number used only once), and the primitives (e.g. CBC) are considered stateful rather than randomized. This is because an IV need not be explicitly forwarded to a recipient but may be derived from a common state updated at both sender and receiver side. (In practice, a short nonce is still transmitted along with the message to consider message loss.) An example of stateful encryption schemes is the counter mode of operation, which has a sequence number for a nonce. The IV size depends on the cryptographic primitive used; for block ciphers it is generally the cipher's block-size. In encryption schemes, the unpredictable part of the IV has at best the same size as the key to compensate for time/memory/data tradeoff attacks. When the IV is chosen at random, the probability of collisions due to the birthday problem must be taken into account. Traditional stream ciphers such as RC4 do not support an explicit IV as input, and a custom solution for incorporating an IV into the cipher's key or internal state is needed. Some designs realized in practice are known to be insecure; the WEP protocol is a notable example, and is prone to related-IV attacks. == Motivation == A block cipher is one of the most basic primitives in cryptography, and frequently used for data encryption. However, by itself, it can only be used to encode a data block of a predefined size, called the block size. For example, a single invocation of the AES algorithm transforms a 128-bit plaintext block into a ciphertext block of 128 bits in size. The key, which is given as one input to the cipher, defines the mapping between plaintext and ciphertext. If data of arbitrary length is to be encrypted, a simple strategy is to split the data into blocks each matching the cipher's block size, and encrypt each block separately using the same key. This method is not secure as equal plaintext blocks get transformed into equal ciphertexts, and a third party observing the encrypted data may easily determine its content even when not knowing the encryption key. To hide patterns in encrypted data while avoiding the re-issuing of a new key after each block cipher invocation, a method is needed to randomize the input data. In 1980, the NIST published a national standard document designated Federal Information Processing Standard (FIPS) PUB 81, which specified four so-called block cipher modes of operation, each describing a different solution for encrypting a set of input blocks. The first mode implements the simple strategy described above, and was specified as the electronic codebook (ECB) mode. In contrast, each of the other modes describe a process where ciphertext from one block encryption step gets intermixed with the data from the next encryption step. To initiate this process, an additional input value is required to be mixed with the first block, and which is referred to as an initialization vector. For example, the cipher-block chaining (CBC) mode requires an unpredictable value, of size equal to the cipher's block size, as additional input. This unpredictable value is added to the first plaintext block before subsequent encryption. In turn, the ciphertext produced in the first encryption step is added to the second plaintext block, and so on. The ultimate goal for encryption schemes is to provide semantic security: by this property, it is practically impossible for an attacker to draw any knowledge from observed ciphertext. It can be shown that each of the three additional modes specified by the NIST are semantically secure under so-called chosen-plaintext attacks. == Properties == Properties of an IV depend on the cryptographic scheme used. A basic requirement is uniqueness, which means that no IV may be reused under the same key. For block ciphers, repeated IV values devolve the encryption scheme into electronic codebook mode: equal IV and equal plaintext result in equal ciphertext. In stream cipher encryption uniqueness is crucially important as plaintext may be trivially recovered otherwise. Example: Stream ciphers encrypt plaintext P to ciphertext C by deriving a key stream K from a given key and IV and computing C as C = P xor K. Assume that an attacker has observed two messages C1 and C2 both encrypted with the same key and IV. Then knowledge of either P1 or P2 reveals the other plaintext since C1 xor C2 = (P1 xor K) xor (P2 xor K) = P1 xor P2. Many schemes require the IV to be unpredictable by an adversary. This is effected by selecting the IV at random or pseudo-randomly. In such schemes, the chance of a duplicate IV is negligible, but the effect of the birthday problem must be considered. As for the uniqueness requirement, a predictable IV may allow recovery of (partial) plaintext. Example: Consider a scenario where a legitimate party called Alice encrypts messages using the cipher-block chaining mode. Consider further that there is an adversary called Eve that can observe these encryptions and is able to forward plaintext messages to Alice for encryption (in other words, Eve is capable of a chosen-plaintext attack). Now assume that Alice has sent a message consisting of an initialization vector IV1 and starting with a ciphertext block CAlice. Let further PAlice denote the first plaintext block of Alice's message, let E denote encryption, and let PEve be Eve's guess for the first plaintext block. Now, if Eve can determine the initialization vector IV2 of the next message she will be able to test her guess by forwarding a plaintext message to Alice starting with (IV2 xor IV1 xor PEve); if her guess was correct this plaintext block will get encrypted to CAlice by Alice. This is because of the following simple observation: CAlice = E(IV1 xor PAlice) = E(IV2 xor (IV2 xor IV1 xor PAlice)). Depending on whether the IV for a cryptographic scheme must be random or only unique the scheme is either called randomized or stateful. While randomized schemes always require the IV chosen by a sender to be forwarded to receivers, stateful schemes allow sender and receiver to share a common IV state, which is updated in a predefined way at both sides. == Block ciphers == Block cipher processing of data is usually described as a mode of operation. Modes are primarily defined for encryption as well as authentication, though newer designs exist that combine both security solutions in so-called authenticated encryption modes. While encryption and authenticated encryption modes usually take an IV matching the cipher's block size, authentication modes are commonly realized as deterministic algorithms, and the IV is set to zero or some other fixed value. == Stream ciphers == In stream ciphers, IVs are loaded into the keyed internal secret state of the cipher, after which a number of cipher rounds are executed prior to releasing the first bit of output. For performance reasons, designers of stream ciphers try to keep that number of rounds as small as possible, but because determining the minimal secure number of rounds for stream ciphers is not a trivial task, and considering other issues such as entropy loss, unique to each cipher construction, related-IVs and other IV-related attacks are a known security issue for stream ciphers, which makes IV loading in stream ciphers a serious concern and a subject of ongoing research. == WEP IV == The 802.11 encryption algorithm called WEP (short for Wired Equivalent Privacy) used a short, 24-bit IV, leading to reused IVs with the same key, which led to it being easily cracked. Packet injection allowed for WEP to be cracked in times as short as several seconds. This ultimately led to the deprecation of WEP. == SSL 2.0 IV == In cipher-block chaining mode (CBC mode), the IV need not be secret, but must be unpredictable (In particular, for any given plaintext, it must not be possible to predict the IV that will be associated to the plaintext in advance of the generation of the IV.) at encryption time. Additionally for the output feedback mode (OFB mode), the IV must be unique. In particular, the (previously) common practice of re-using the last ciphertext block of a message as the IV for the next message is insecure (for example, this method was used by SSL 2.0). If an attacker knows
Data verification
Data verification is a process in which different types of data are checked for accuracy and inconsistencies after data migration is done. In some domains it is referred to Source Data Verification (SDV), such as in clinical trials. Data verification helps to determine whether data was accurately translated when data is transferred from one source to another, is complete, and supports processes in the new system. During verification, there may be a need for a parallel run of both systems to identify areas of disparity and forestall erroneous data loss. Methods for data verification include double data entry, proofreading and automated verification of data. Proofreading data involves someone checking the data entered against the original document. This is also time-consuming and costly. Automated verification of data can be achieved using one way hashes locally or through use of a SaaS based service such as Q by SoLVBL to provide immutable seals to allow verification of the original data.
Virtual collective consciousness
Virtual collective consciousness (VCC) is a term rebooted and promoted by two behavioral scientists, Yousri Marzouki and Olivier Oullier in their 2012 Huffington Post article titled: "Revolutionizing Revolutions: Virtual Collective Consciousness and the Arab Spring", after its first appearance in 1999-2000. VCC is now defined as an internal knowledge catalyzed by social media platforms and shared by a plurality of individuals driven by the spontaneity, the homogeneity, and the synchronicity of their online actions. VCC occurs when a large group of persons, brought together by a social media platform think and act with one mind and share collective emotions. Thus, they are able to coordinate their efforts efficiently, and could rapidly spread their word to a worldwide audience. When interviewed about the concept of VCC that appeared in the book - Hyperconnectivity and the Future of Internet Communication - he edited, Professor of Pervasive Computing, Adrian David Cheok mentioned the following: "The idea of a global (collective) virtual consciousness is a bottom-up process and a rather emergent property resulting from a momentum of complex interactions taking place in social networks. This kind of collective behaviour (or intelligence) results from a collision between a physical world and a virtual world and can have a real impact in our life by driving collective action." == Etymology == In 1999-2000, Richard Glen Boire provided a cursory mention and the only occurrence of the term "Virtual collective consciousness" in his text as follows: The trend of technology is to overcome the limitations of the human body. And, the Web has been characterized as a virtual collective consciousness and unconsciousness The recent definition of VCC evolved from the first empirical study that provided a cyberpsychological insight into the contribution of Facebook to the 2011 Tunisian revolution. In this study, the concept was originally called "collective cyberconsciousness". The latter is an extension of the idea of "collective consciousness" coupled with "citizen media" usage. The authors of this study also made a parallel between this original definition of VCC and other comparable concepts such as Durkheim's collective representation, Žižek's "collective mind" or Boguta's "new collective consciousness" that he used to describe the computational history of the Internet shutdown during the Egyptian revolution. Since VCC is the byproduct of the network's successful actions, then these actions must be timely, acute, rapid, domain-specific, and purpose-oriented to successfully achieve their goal. Before reaching a momentum of complexity, each collective behavior starts by a spark that triggers a chain of events leading to a crystallized stance of a tremendous amount of interactions. Thus, VCC is an emergent global pattern from these individual actions. In 2012, the term virtual collective consciousness resurfaced and was brought to light after extending its applications to the Egyptian case and the whole social networking major impact on the success of the so-called Arab Spring. Moreover, the acronym VCC was suggested to identify the theoretical framework covering on-line behaviors leading to a virtual collective consciousness. Hence, online social networks have provided a new and faster way of establishing or modifying "collective consciousness" that was paramount to the 2011 uprisings in the Arab world. == Theoretical underpinnings of VCC == Various theoretical references in fields ranging from sociology to computer science were mentioned in order to account for the key features that render the framework for a virtual collective consciousness. The following list is not exhaustive, but the references it contains are often highlighted: Émile Durkheim's collective representations are at the heart of VCC since collectivity taken decisions according to Durkheim's assumptions will approve or disapprove individuals' actions and help them eventually reach their final goal. Marshall McLuhan's global village: The shrinking of our big world to a small place called cyberspace is made possible by technological extensions of human consciousness. Carl Jung's collective unconscious: When a society witnesses significant changes, the anchoring of archetypal images (e.g., political leaders) seems to be deeply rooted in individuals' collective unconscious that is likely to bias their political choices. Individual memories of public events were also supposed to convey a "collective awareness" that can be subconsciously altered by the instantaneous spread of information through social networking around the world. Daniel Wegner's transactive memory (TM): social-networking platforms such as Facebook during the Tunisian revolution or Twitter during the Egyptian revolution served as placeholders of a VCC where information can be harnessed and steered to the highly specific revolutionary purpose. Although research on TM was originally limited to couples, small groups, and organizations, recent studies strongly suggest that an effective TM can operate on a very large scale too. James Surowiecki's wisdom of crowds Collective influence algorithm: The CI (Collective influence) algorithm is effective in finding influential nodes in a variety of networks, including social networks, communication networks, and biological networks. It has been used to identify influencers on social-media platforms, to identify key nodes in transportation networks, and to identify potential drug-targets in biological networks. == Some illustrations of VCC == Besides the studied effect of social networking on the Tunisian and Egyptian revolutions, the former via Facebook and the latter via Twitter other applications were studied under the prism of VCC framework: The Whitacre's virtual choir: A compelling example of the degree of autonomy and self-identity members of a spontaneously created network through a VCC is Eric Whitacre's unique musical project that involved a collection of singers performing remotely to create a virtual Choir. The effect of all the voices illustrated a genuine virtual collective empathy merging the artist's mind with all the singers through his silent conducting gestures. The Harlem Shake dance: The Bitcoin protocol: It was questioned whether or not the Bitcoin protocol can morph into virtual collective consciousness. The Byzantine generals problem was used as an analogy to understand the behavioral complexity of the community of Bitcoin's users. Artificial Social Networking Intelligence (ASNI): refers to the application of artificial intelligence within social networking services and social media platforms. It encompasses various technologies and techniques used to automate, personalize, enhance, improve, and synchronize users' interactions and experiences within social networks. ASNI is expected to evolve rapidly, influencing how we interact online and shaping our digital experiences. Transparency, ethical considerations, media influence bias, and user control over data will be crucial to ensure responsible development and positive impact.
Pedagogical agent
A pedagogical agent is a concept borrowed from computer science and artificial intelligence and applied to education, usually as part of an intelligent tutoring system (ITS). It is a simulated human-like interface between the learner and the content, in an educational environment. A pedagogical agent is designed to model the type of interactions between a student and another person. Mabanza and de Wet define it as "a character enacted by a computer that interacts with the user in a socially engaging manner". A pedagogical agent can be assigned different roles in the learning environment, such as tutor or co-learner, depending on the desired purpose of the agent. "A tutor agent plays the role of a teacher, while a co-learner agent plays the role of a learning companion". == History == The history of Pedagogical Agents is closely aligned with the history of computer animation. As computer animation progressed, it was adopted by educators to enhance computerized learning by including a lifelike interface between the program and the learner. The first versions of a pedagogical agent were more cartoon than person, like Microsoft's Clippy which helped users of Microsoft Office load and use the program's features in 1997. However, with developments in computer animation, pedagogical agents can now look lifelike. By 2006 there was a call to develop modular, reusable agents to decrease the time and expertise required to create a pedagogical agent. There was also a call in 2009 to enact agent standards. The standardization and re-usability of pedagogical agents is less of an issue since the decrease in cost and widespread availability of animation tools. Individualized pedagogical agents can be found across disciplines including medicine, math, law, language learning, automotive, and armed forces. They are used in applications directed to every age, from preschool to adult. == Learning theories related to pedagogical agent design == === Distributed cognition theory === Distributed cognition theory is the method in which cognition progresses in the context of collaboration with others. Pedagogical agents can be designed to assist the cognitive transfer to the learner, operating as artifacts or partners with collaborative role in learning. To support the performance of an action by the user, the pedagogical agent can act as a cognitive tool as long as the agent is equipped with the knowledge that the user lacks. The interactions between the user and the pedagogical agent can facilitate a social relationship. The pedagogical agent may fulfill the role of a working partner. === Socio-cultural learning theory === Socio-cultural learning theory is how the user develops when they are involved in learning activities in which there is interaction with other agents. A pedagogical agent can: intervene when the user requests, provide support for tasks that the user cannot address, and potentially extend the learners cognitive reach. Interaction with the pedagogical agent may elicit a variety of emotions from the learner. The learner may become excited, confused, frustrated, and/or discouraged. These emotions affect the learners' motivation. === Extraneous Cognitive Load === Extraneous cognitive load is the extra effort being exerted by an individual's working memory due to the way information is being presented. A pedagogical agent can increase the user's cognitive load by distracting them and becoming the focus of their attention, causing split attention between the instructional material and the agent. Agents can reduce the perceived cognitive load by providing narration and personalization that can also promote a user's interest and motivation. While research on the reduction of cognitive load from pedagogical agents is minimal, more studies have shown that agents do not increase it. == Effectiveness == It has been suggested by researchers that pedagogical agents may take on different roles in the learning environment. Examples of these roles are: supplanting, scaffolding, coaching, testing, or demonstrating or modelling a procedure. A pedagogical agent as a tutor has not been demonstrated to add any benefit to an educational strategy in equivalent lessons with and without a pedagogical agent. According to Richard Mayer, there is some support in research for pedagogical agent increasing learning, but only as a presenter of social cues. A co-learner pedagogical agent is believed to increase the student's self-efficacy. By pointing out important features of instructional content, a pedagogical agent can fulfill the signaling function, which research on multimedia learning has shown to enhance learning. Research has demonstrated that human-human interaction may not be completely replaced by pedagogical agents, but learners may prefer the agents to non-agent multimedia systems. This finding is supported by social agency theory. Much like the varying effectiveness of the pedagogical agent roles in the learning environment, agents that take into account the user's affect have had mixed results. Research has shown pedagogical agents that make use of the users’ affect have been found to increase user knowledge retention, motivation, and perceived self-efficacy. However, with such a broad range of modalities in affective expressions, it is often difficult to utilize them. Additionally, having agents detect a user's affective state with precision remains challenging, as displays of affect are different across individuals. == Design == === Attractiveness === The appearance of a pedagogical agent can be manipulated to meet the learning requirements. The attractiveness of a pedagogical agent can enhance student's learning when the users were the opposite gender of the pedagogical agent. Male students prefer a sexy appearance of a female pedagogical agents and dislike the sexy appearance of male agents. Female students were not attracted by the sexy appearance of either male or female pedagogical agents. === Affective Response === Pedagogical agents have reached a point where they can convey and elicit emotion, but also reason about and respond to it. These agents are often designed to elicit and respond to affective actions from users through various modalities such as speech, facial expressions, and body gestures. They respond to the affective state of the given user, and make use of these modalities using a wide array of sensors incorporated into the design of the agent. Specifically in education and training applications, pedagogical agents are often designed to increasingly recognize when users or learners exhibit frustration, boredom, confusion, and states of flow. The added recognition in these agents is a step toward making them more emotionally intelligent, comforting and motivating the users as they interact. === Digital Representation === The design of a pedagogical agent often begins with its digital representation, whether it will be 2D or 3D and static or animated. Several studies have developed pedagogical agents that were both static and animated, then evaluated the relative benefits. Similar to other design considerations, the improved learning from static or animated agents remains questionable. One study showed that the appearance of an agent portrayed using a static image can impact a user's recall, based on the visual appearance. Other research found results that suggest static agent images improve learning outcomes. However, several other studies found user's learned more when the pedagogical agent was animated rather than static. Recently a meta-analysis of such research found a negligible improvement in learning via pedagogical agents, suggesting more work needs to be done in the area to support any claims.
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