The primary value learned value (PVLV) model is a possible explanation for the reward-predictive firing properties of dopamine (DA) neurons. It simulates behavioral and neural data on Pavlovian conditioning and the midbrain dopaminergic neurons that fire in proportion to unexpected rewards. It is an alternative to the temporal-differences (TD) algorithm. It is used as part of Leabra.
Adversarial stylometry
Adversarial stylometry is the practice of altering writing style to reduce the potential for stylometry to discover the author's identity or their characteristics. This task is also known as authorship obfuscation or authorship anonymisation. Stylometry poses a significant privacy challenge in its ability to unmask anonymous authors or to link pseudonyms to an author's other identities, which, for example, creates difficulties for whistleblowers, activists, and hoaxers and fraudsters. The privacy risk is expected to grow as machine learning techniques and text corpora develop. All adversarial stylometry shares the core idea of faithfully paraphrasing the source text so that the meaning is unchanged but the stylistic signals are obscured. Such a faithful paraphrase is an adversarial example for a stylometric classifier. Several broad approaches to this exist, with some overlap: imitation, substituting the author's own style for another's; translation, applying machine translation with the hope that this eliminates characteristic style in the source text; and obfuscation, deliberately modifying a text's style to make it not resemble the author's own. Manually obscuring style is possible, but laborious; in some circumstances, it is preferable or necessary. Automated tooling, either semi- or fully-automatic, could assist an author. How best to perform the task and the design of such tools is an open research question. While some approaches have been shown to be able to defeat particular stylometric analyses, particularly those that do not account for the potential of adversariality, establishing safety in the face of unknown analyses is an issue. Ensuring the faithfulness of the paraphrase is a critical challenge for automated tools. It is uncertain if the practice of adversarial stylometry is detectable in itself. Some studies have found that particular methods produced signals in the output text, but a stylometrist who is uncertain of what methods may have been used may not be able to reliably detect them. == History == Rao & Rohatgi (2000), an early work in adversarial stylometry, identified machine translation as a possibility, but noted that the quality of translators available at the time presented severe challenges. Kacmarcik & Gamon (2006) is another early work. Brennan, Afroz & Greenstadt (2012) performed the first evaluation of adversarial stylometric methods on actual texts. Brennan & Greenstadt (2009) introduced the first corpus of adversarially authored texts specifically for evaluating stylometric methods; other corpora include the International Imitation Hemingway Competition, the Faux Faulkner contest, and the hoax blog A Gay Girl in Damascus. == Motivations == Rao & Rohatgi (2000) suggest that short, unattributed documents (i.e., anonymous posts) are not at risk of stylometric identification, but pseudonymous authors who have not practiced adversarial stylometry in producing corpuses of thousands of words may be vulnerable. Narayanan et al. (2012) attempted large-scale deanonymisation of 100,000 blog authors with mixed results: the identifications were significantly better than chance, but only accurately matched the blog and author a fifth of the time; identification improved with the number of posts written by the author in the corpus. Even if an author is not identified, some of their characteristics may still be deduced stylometrically, or stylometry may narrow the anonymity set of potential authors sufficiently for other information to complete the identification. Detecting author characteristics (e.g., gender or age) is often simpler than identifying an author from a large, possibly open, set of candidates. Modern machine learning techniques offer powerful tools for identification; further development of corpora and computational stylometric techniques are likely to raise further privacy issues. Gröndahl & Asokan (2020a) say that the general validity of the hypothesis underlying stylometry—that authors have invariant, content-independent 'style fingerprints'—is uncertain, but "the deanonymisation attack is a real privacy concern". Those interested in practicing adversarial stylometry and stylistic deception include whistleblowers avoiding retribution; journalists and activists; perpetrators of frauds and hoaxes; authors of fake reviews; literary forgers; criminals disguising their identity from investigators; and, generally, anyone with a desire for anonymity or pseudonymity. Authors, or agents acting on behalf of authors, may also attempt to remove stylistic clues to author characteristics (e.g., race or gender) so that knowledge of those characteristics cannot be used for discrimination (e.g., through algorithmic bias). Another possible use for adversarial stylometry is in disguising automatically generated text as human-authored. == Methods == With imitation, the author attempts to mislead stylometry by matching their style to another author's. An incomplete imitation, where some of the true author's unique characteristics appear alongside the imitated author's, can be a detectable signal for the use of adversarial stylometry. Imitation can be performed automatically with style transfer systems, though this typically requires a large corpus in the target style for the system to learn from. Another approach is translation, which employs machine translation of a source text to eliminate characteristic style, often through multiple translators in sequence to produce a round-trip translation. Such chained translation can lead to texts being significantly altered, even to the point of incomprehensibility; improved translation tools reduce this risk. More simply-structured texts can be easier to machine translate without losing the original meaning. Machine translation blurs into direct stylistic imitation or obfuscation achieved through automated style transfer, which can be viewed as a "translation" with the same language as input and output. With low-quality translation tools, an author can be required to manually correct major translation errors while avoiding the hazard of re-introducing stylistic characteristics. Wang, Juola & Riddell (2022) found that gross errors introduced by Google Translate were rare, but more common with several intermediate translations—however, occasional simple or short sentences and misspellings in the source text appeared verbatim in the output, potentially providing an identifying signal. Chain translation can leave characteristic traces of its application in a document, which may allow reconstruction of the intermediate languages used and the number of translation steps performed. Obfuscation involves deliberately changing the style of a text to reduce its similarity to other texts by some metric; this may be performed at the time of writing by conscious modification, or as part of a revision process with feedback from the metric being targeted as an input to decide when the text has been sufficiently obfuscated. In contrast to translation, complex texts can offer more opportunities for effective obfuscation without altering meaning, and likewise genres with more permissible variation allow more obfuscation. However, longer texts are harder to thoroughly obfuscate. Obfuscation can blend into imitation if the author develops a novel target style, distinct from their original style. With respect to masking author characteristics, obfuscation may aim to achieve a union (adding signals for imitated characteristics) or an intersection (removing signals and normalising) of other authors' styles. Avoiding the author's own idiosyncrasies and producing a "normalised" text is a critical obfuscatory step: an author may have a unique tendency to misspell certain words, use particular variants, or to format a document in a characteristic way. Stylometric signals vary in how simply they can be adversarially masked; an author may easily change their vocabulary by conscious choice, but altering the pattern of grammar or the letter frequency in their text may be harder to achieve, though Juola & Vescovi (2011) report that imitation typically succeeds at masking more characteristics than obfuscation. Automated obfuscation may require large amounts of training data written by the author. Concerning automated implementations of adversarial stylometry, two possible implementations are rule-based systems for paraphrasing; and encoder–decoder architectures, where the text passes through an intermediate format that is (intended to be) style-neutral. Another division in automated methods is whether there is feedback from an identification system or not. With such feedback, finding paraphrases for author masking has been characterised as a heuristic search problem, exploring textual variants until the result is stylistically sufficiently far (in the case of obfuscation) or near (in the case of imitation), which then constitutes an adversarial example for that identification system. == Evaluation == How
Vintage computer
A vintage computer is an older computer system that is largely regarded as obsolete. The personal computer has been around since around 1971, and in that time technological advancement means existing models get replaced every few years. Nevertheless, these otherwise useless computers have spawned a sub-culture of vintage computer collectors who often spend large sums for the rarest examples, not only to display but functionally restore. This involves active software development and adaptation to modern uses. This often includes homebrew developers and hackers who add on, update and create hybrid composites from new and old computers for uses they were otherwise never intended. Ethernet interfaces have been designed for many vintage 8-bit machines to allow limited connectivity to the Internet, where users can access discussion groups, bulletin boards, and software databases. Most of this hobby centers on computers made after 1960, though some collectors also specialize in older computers. The Vintage Computer Festival, an event held by the Vintage Computer Federation for the exhibition and celebration of vintage computers, has been held annually since 1997 and has expanded internationally. == By platform == === MITS Inc. === Micro Instrumentation and Telemetry Systems (MITS) produced the Altair 8800 in 1975. According to Harry Garland, the Altair 8800 was the product that catalyzed the microcomputer revolution of the 1970s. === IMSAI === The IMSAI 8080 is a clone of the Altair 8800. It was introduced in 1975, first as a kit, and later as an assembled system. The list price was $591 (equivalent to $3,584 in 2025) for a kit, and $931 (equivalent to $5,570 in 2025) assembled. === Processor Technology === Processor Technology produced the Sol-20. This was one of the first machines to have a case that included a keyboard; a design feature copied by many of later "home computers". === SWTPC === Southwest Technical Products Corporation (SWTPC) produced the 8-bit SWTPC 6800 and later the 16-bit SWTPC 6809 kits that employed the Motorola 68xx series microprocessors. === Apple Inc. === The earliest Apple Inc. personal computers, using the MOS Technology 6502 processors, are among some of the most collectible. They are relatively easy to maintain in an operational state thanks to Apple's use of readily available off-the-shelf parts. Apple I (1976): The Apple-1 was Apple's first product and has brought some of the highest prices ever paid for a microcomputer at auction. Apple II (1977): The Apple II series of computers are some of the easiest to adapt, thanks to the original expansion architecture designed for them. New peripheral cards are still being designed by an avid thriving community, thanks to the longevity of this platform, manufactured from 1977 through 1993. Numerous websites exist to support not only legacy users but new adopters who weren't even born when the Apple II was discontinued by Apple. Macintosh (1984): The original Macintosh used a 32-bit Motorola 68000 processor running at 7.8336 MHz and came with 128 KB of RAM. The list price was $2495 (equivalent to $7,732 in 2025).Perhaps because of its friendly design and first commercially successful graphical user interface as well as its enduring Finder application that persists on the most current Macs, the Macintosh is one of the most collected and used vintage computers. With dozens of websites around the world, old Macintosh hardware and software are input into daily use. The Macintosh had a strong presence in many early computer labs, creating a nostalgia factor for former students who recall their first computing experiences. === RCA === The COSMAC Elf in 1976 was an inexpensive (about $100) single-board computer that was easily built by hobbyists. Many people who could not afford an Altair could afford an ELF, which was based on the RCA 1802 chip. Because the chips are still available from other sources, modern recreations of the ELF are fairly common and there are several fan websites. === IBM === The IBM 1130 (1965) was a desk-sized small computer. It was the often the first computer used by many college students, still has a following of interested users. Most of the remaining 1130 systems in 2023 are in museums, but an emulator is available for users who don't have access to a physical 1130. The 5100 also has an avid collector and fan base. The PC series (5150 PC, 5155 Portable PC, 5160 PC/XT, 5170 PC/AT) has become very popular in recent years, with the earliest models (PC) being considered the most collectible. === Acorn BBC & Archimedes === The Acorn BBC Micro was a very popular British computer in the 1980s with home and educational users and enjoyed near-universal usage in British schools into the mid-1990s. It was possible to use 100K 5+1⁄4-inch disks, and it had many expansion ports. The Archimedes series – the de facto successor to the BBC Micro – has also enjoyed a following in recent years, thanks to its status as the first computer to be based around ARM's RISC microprocessor. === Tandy/Radio Shack === The Tandy/RadioShack Model 100 is still widely collected and used as one of the earliest examples of a truly portable computer. Other Tandy offerings, such as the TRS-80 line, are also very popular, and early systems, like the Model I, in good condition can command premium prices on the vintage computer market. === Sinclair === The Sinclair ZX81 and ZX Spectrum series were the most popular British home computers of the early 1980s, with a wide choice of emulators available for both platforms. The Spectrum in particular enjoys a cult following due to its popularity as a games platform, with new games titles still being developed even today. Original "rubber key" Spectrums fetch the highest prices on the second-hand market, with the later Amstrad-built models attracting less of a following. The earlier ZX81 is not as popular in original hardware form due to its monochrome display and limited abilities next to the Spectrum, but still unassembled ZX81 kits still appear on eBay occasionally. === MSX === Although nearly nonexistent in the United States, the MSX architecture has strong communities of fans and hobbyists worldwide, particularly in Japan (where the standard was conceived and developed), South Korea (the only country that had an MSX-based game console, Zemmix), Netherlands, Spain, Brazil, Argentina, Russia, Chile, the Middle East, and others. New hardware and software are being actively developed to this day as well. One of the latest fundamental (from hardware and software perspectives) revivals of the MSX is the GR8BIT. === Robotron === The Robotron Z1013 was an East German home computer produced by VEB Robotron. It had a U880 processor, 16 KB RAM, and a membrane keyboard. The KC 85 series of computers was a modular 8-bit computer system used in East German schools. === Commodore === VIC-20 Commodore 64 Commodore PET Amiga === Xerox === The Xerox Alto, designed and manufactured by Xerox PARC and released in 1973, was the first personal computer equipped with a graphic user interface. In 1979, Steve Jobs of Apple Inc. arranged for his engineers to visit Xerox in order to see the Alto. The design concepts of the Alto soon appeared in the Apple Lisa and Macintosh systems. The Xerox Star, also known as the 8010/40, was made available in 1981. It followed on the Alto. Like the Alto, this machine was expensive and was only intended for corporate office usage. Therefore, being out of the price range of the average user, this product had little market penetration. === Silicon Graphics === The SGI Indy, built in 1993 for Silicon Graphics has a history of usage in the development of the Nintendo 64 as well as various CGI projects throughout the 1990s and early 2000s. The Indy and other machines in the SGI lineup have remained cult classics.
Attention inequality
Attention inequality is the inequality of distribution of attention across users on social networks, people in general, and for scientific papers. Yun Family Foundation introduced "Attention Inequality Coefficient" as a measure of inequality in attention and arguments it by the close interconnection with wealth inequality. == Relationship to economic inequality == Attention inequality is related to economic inequality since attention is an economically scarce good. The same measures and concepts as in classical economy can be applied for attention economy. The relationship develops also beyond the conceptual level—considering the AIDA process, attention is the prerequisite for real monetary income on the Internet. On data of 2018, a significant relationship between likes and comments on Facebook to donations is proven for non-profit organizations. == Attention economy == The attention economy refers to the practice of maximizing the attention users give to a product for advertising-related reasons. Attention economy remains one of the most common forms of advertising, and has been steadily increasing thanks to new technologies such as television, internet and social media. It is one of the most widely-used approaches to economy for its effectiveness for maximising the noticeability of a certain product. == Attention inequality in social media == In social media, attention inequality refers to the unequal distribution of users' attention on social media platforms. This means that instead of an equal distribution of attention, fewer sources receive a disproportionate share of attention, leaving many unnoticed. This phenomenon is possibly the result of social media algorithms, which are commonly designed to drive maximum engagement. This phenomenon is a large factor in the polarization and creation of echo-chambers. Social media algorithms tend to note content that is already performing well and display it to more users, while content that is equally engaging or well-made is not recommended to users. Posts that trigger strong emotions usually out-perform more "uncontroversial" content. When many users interact with the post, it signals the algorithm that the specific post drives engagement. The algorithm then tends to recommend that type of content to an exponential number of people, potentially outperforming "un-emotional" content. These factors, when combined, tend to create an unequal social media environment. == Attention inequality in science == According to a recent 2025 study about research inequality among scientists published in Information Processing and Management, scientific discourse is restricted to a small group of connected scientists, and is frequently not an accurate representation of the whole scientific community. Using citation-network analysis in the fields of nanoscience and chemical physics, the study claims that a group of connected scientists has a significant notability in the scientific community. The calculated connection strength between these scientists is estimated to be about 4.5, the study also says that these authors cite each other four times more often than would be predicted in a random network, whereas ordinary scientists that exist outside of this group only reach an estimated connection strength of 0.9. The study findings suggest that that scientific attention is not distributed by merit, but rather by the connectedness of the scientists involved in the research. == Extent == As data of 2008 shows, 50% of the attention is concentrated on approximately 0.2% of all hostnames, and 80% on 5% of hostnames. The Gini coefficient of attention distribution lay in 2008 at over 0.921 for such commercial domains names as ac.jp and at 0.985 for .org-domains. The Gini coefficient was measured on Twitter in 2016 for the number of followers as 0.9412, for the number of mentions as 0.9133, and for the number of retweets as 0.9034. For comparison, the world's income Gini coefficient was 0.68 in 2005 and 0.904 in 2018. More than 96% of all followers, 93% of the retweets, and 93% of all mentions are owned by 20% of Twitter. == Causes == At least for scientific papers, today's consensus states that inequality is unexplainable by variations of quality and individual talent. The Matthew effect plays a significant role in the emergence of attention inequality—those who already enjoy large amounts of attention get even more attention, and those who do not lose even more. Ranking algorithms based on relevance to the user have been found to alleviate the inequality of the number of posts across topics.
AMiner (database)
AMiner (formerly ArnetMiner) is a free online service used to index, search, and mine big scientific data. == Overview == AMiner (ArnetMiner) is designed to search and perform data mining operations against academic publications on the Internet, using social network analysis to identify connections between researchers, conferences, and publications. This allows it to provide services such as expert finding, geographic search, trend analysis, reviewer recommendation, association search, course search, academic performance evaluation, and topic modeling. AMiner was created as a research project in social influence analysis, social network ranking, and social network extraction. A number of peer-reviewed papers have been published arising from the development of the system. It has been in operation for more than three years, and has indexed 130,000,000 researchers and more than 265 million publications. The research was funded by the Chinese National High-tech R&D Program and the National Science Foundation of China. AMiner is commonly used in academia to identify relationships between and draw statistical correlations about research and researchers. It has attracted more than 10 million independent IP accesses from 220 countries and regions. The product has been used in Elsevier's SciVerse platform, and academic conferences such as SIGKDD, ICDM, PKDD, WSDM. == Operation == AMiner automatically extracts the researcher profile from the web. It collects and identifies the relevant pages, then uses a unified approach to extract data from the identified documents. It also extracts publications from online digital libraries using heuristic rules. It integrates the extracted researchers’ profiles and the extracted publications. It employs the researcher name as the identifier. A probabilistic framework has been proposed to deal with the name ambiguity problem in the integration. The integrated data is stored into a researcher network knowledge base (RNKB). The principal other product in the area are Google Scholar, Elsevier's Scirus, and the open source project CiteSeer. == History == It was initiated and created by professor Jie Tang from Tsinghua University, China. It was first launched in March 2006. The following provide a list of updates in the past years: March 2006, Version 0.1, Functions include researcher profiling, expert search, conference search, and publication search. The system was developed in Perl; August 2006, Version 1.0, The system was re-implemented in Java; July 2007, Version 2.0, New functions include researcher interest mining, association search, survey paper finding (unavailable now); April 2008, Version 3.0, New functions include query understanding, new GUI, and search log analysis; November 2008, Version 4.0, New functions include graph search, topic modeling, NSF/NSFC funding information extraction; April 2009, Version 5.0, New functions include Profile edition, open API service, Bole search, course search (unavailable now); December 2009, Version 6.0, New functions include academic performance evaluation, user feedback, conference analysis; May 2010, Version 7.0, New functions include name disambiguation, paper-reviewer recommendation, ArnetPage creation; March 2012, Version II, renamed as AMiner, rewrote all the codes and redesign the GUI. New functions include: geographic search, ArnetAPP platform. June 2014, Version II, renamed as AMiner, rewrote all the codes and redesign the GUI. New functions include: geographic search, ArnetAPP platform. December 2015, a completely new version got online. May 2017, professional version got online. April 2018, New functions include Trend Analysis, a deep learning based Name Disambiguation == Resources == AMiner published several datasets for academic research purpose, including Open Academic Graph, DBLP+citation (a data set augmenting citations into the DBLP data from Digital Bibliography & Library Project), Name Disambiguation, Social Tie Analysis. For more available datasets and source codes for research, please refer to.
Artificial Linguistic Internet Computer Entity
A.L.I.C.E. (Artificial Linguistic Internet Computer Entity), also referred to as Alicebot, or simply Alice, is a natural language processing chatbot—a program that engages in a conversation with a human by applying some heuristical pattern matching rules to the human's input. It was inspired by Joseph Weizenbaum's classical ELIZA program. It is one of the strongest programs of its type and has won the Loebner Prize, awarded to accomplished humanoid, talking robots, three times (in 2000, 2001, and 2004). The program is unable to pass the Turing test, as even the casual user will often expose its mechanistic aspects in short conversations. Alice was originally composed by Richard Wallace; it "came to life" on November 23, 1995. The program was rewritten in Java beginning in 1998. The current incarnation of the Java implementation is Program D. The program uses an XML Schema called AIML (Artificial Intelligence Markup Language) for specifying the heuristic conversation rules. Alice code has been reported to be available as open source. The AIML source is available from ALICE A.I. Foundation on Google Code and from the GitHub account of Richard Wallace. These AIML files can be run using an AIML interpreter like Program O or Program AB. == In popular culture == Spike Jonze has cited ALICE as the inspiration for his academy award-winning film Her, in which a human falls in love with a chatbot. In a New Yorker article titled “Can Humans Fall in Love with Bots?” Jonze said “that the idea originated from a program he tried about a decade ago called the ALICE bot, which engages in friendly conversation.” The Los Angeles Times reported:Though the film’s premise evokes comparisons to Siri, Jonze said he actually had the idea well before the Apple digital assistant came along, after using a program called Alicebot about ten years ago. As geek nostalgists will recall, that intriguing if at times crude software (it flunked the industry-standard Turing Test) would attempt to engage users in everyday chatter based on a database of prior conversations. Jonze liked it, and decided to apply a film genre to it. “I thought about that idea, and what if you had a real relationship with it?” Jonze told reporters. “And I used that as a way to write a relationship movie and a love story.”
End-to-end encryption
End-to-end encryption (E2EE) is a method of implementing a secure communication system where only the sender and intended recipient can read the messages. No one else, including the system provider, telecom providers, Internet providers or malicious actors, can access the cryptographic keys needed to read or send messages. End-to-end encryption prevents data from being read or secretly modified, except by the sender and intended recipients. In many applications, messages are relayed from a sender to some recipients by a service provider. In an E2EE-enabled service, messages are encrypted on the sender's device such that no third party, including the service provider, has the means to decrypt them. The recipients retrieve encrypted messages and decrypt them independently on their own devices. Since third parties cannot decrypt the data being communicated or stored, services with E2EE are better at protecting user data from data breaches and espionage. Computer security experts, digital freedom organizations, and human rights activists advocate for the use of E2EE due to its security and privacy benefits, including its ability to resist mass surveillance. Popular messaging apps like WhatsApp, iMessage, Facebook Messenger, and Signal use end-to-end encryption for chat messages, with some also supporting E2EE of voice and video calls. As of May 2025, WhatsApp is the most widely used E2EE messaging service, with over 3 billion users. Meanwhile, Signal with an estimated 70 million users, is regarded as the current gold standard in secure messaging by cryptographers, protestors, and journalists. Since end-to-end encrypted services cannot offer decrypted messages in response to government requests, the proliferation of E2EE has been met with controversy. Around the world, governments, law enforcement agencies, and child protection groups have expressed concerns over its impact on criminal investigations. As of 2025, some governments have successfully passed legislation targeting E2EE, such as Australia's Telecommunications and Other Legislation Amendment Act (2018) and the Online Safety Act (2023) in the UK. Other attempts at restricting E2EE include the EARN IT Act in the US and the Child Sexual Abuse Regulation in the EU.[1] Nevertheless, some government bodies such as the UK's Information Commissioner's Office and the US's Cybersecurity and Infrastructure Security Agency (CISA) have argued for the use of E2EE, with Jeff Greene of the CISA advising that "encryption is your friend" following the discovery of the Salt Typhoon espionage campaign in 2024. == Definitions == End-to-end encryption is a means of ensuring the security of communications in applications like secure messaging. Under E2EE, messages are encrypted on the sender's device such that they can be decoded only by the final recipient's device. In many non-E2EE messaging systems, including email and many chat platforms, messages pass through intermediaries and are stored by a third party service provider, from which they are retrieved by the recipient. Even if messages are encrypted, they are only encrypted 'in transit', and are thus accessible by the service provider. Server-side disk encryption is also distinct from E2EE because it does not prevent the service provider from viewing the information, as they have the encryption keys and can simply decrypt it. The term "end-to-end encryption" originally only meant that the communication is never decrypted during its transport from the sender to the receiver. For example, around 2003, E2EE was proposed as an additional layer of encryption for GSM or TETRA, in addition to the existing radio encryption protecting the communication between the mobile device and the network infrastructure. This has been standardized by SFPG for TETRA. Note that in TETRA, the keys are generated by a Key Management Centre (KMC) or a Key Management Facility (KMF), not by the communicating users. Later, around 2014, the meaning of "end-to-end encryption" started to evolve when WhatsApp encrypted a portion of its network, requiring that not only the communication stays encrypted during transport, but also that the provider of the communication service is not able to decrypt the communications—maliciously or when requested by law enforcement agencies. Similarly, messages must be undecryptable in transit by attackers through man-in-the-middle attacks. This new meaning is now the widely accepted one. == Motivations == The lack of end-to-end encryption can allow service providers to easily provide search and other features, or to scan for illegal and unacceptable content. However, it also means that content can be read by anyone who has access to the data stored by the service provider, by design or via a backdoor. This can be a concern in many cases where privacy is important, such as in governmental and military communications, financial transactions, and when sensitive information such as health and biometric data are sent. If this content were shared without E2EE, a malicious actor or adversarial government could obtain it through unauthorized access or subpoenas targeted at the service provider. E2EE alone does not guarantee privacy or security. For example, the data may be held unencrypted on the user's own device or accessed through their own app if their credentials are compromised. == Modern implementations == === Messaging === In May 2026, Meta ended support for end-to-end encryption (E2EE) on Instagram, reversing a previous commitment to expand the technology across its messaging services. The company justified the move as a measure to mitigate fraudulent activity and facilitate the detection of harmful content. The decision highlighted a conflict between digital privacy and online safety; while child protection organizations supported the change to better identify predatory behavior, privacy advocates argued that removing E2EE compromises user security. As of 2025, messaging apps like Signal and WhatsApp are designed to exclusively use end-to-end encryption. Both Signal and WhatsApp use the Signal Protocol. Other messaging apps and protocols that support end-to-end encryption include Facebook Messenger, iMessage, Telegram, Matrix, and Keybase. Although Telegram supports end-to-end encryption, it has been criticized for not enabling it by default, instead supporting E2EE through opt-in "secret chats". As of 2020, Telegram did not support E2EE for group chats and no E2EE on its desktop clients. In 2022, after controversy over the use of Facebook Messenger messages in an abortion lawsuit in Nebraska, Facebook added support for end-to-end encryption in the Messenger app. Writing for Wired, technologist Albert Fox Cahn criticized Messenger's approach to end-to-end encryption, which required the user to opt into E2EE for each conversation and split the message thread into two chats which were easy for users to confuse. In December 2023, Facebook announced plans to enable end-to-end encryption by default despite pressure from British law enforcement agencies. As of 2016, many server-based communications systems did not include end-to-end encryption. These systems can only guarantee the protection of communications between clients and servers, meaning that users have to trust the third parties who are running the servers with the sensitive content. End-to-end encryption is regarded as safer because it reduces the number of parties who might be able to interfere or break the encryption. In the case of instant messaging, users may use a third-party client or plugin to implement an end-to-end encryption scheme over an otherwise non-E2EE protocol. === Audio and video conferencing === Signal and WhatsApp use end-to-end encryption for audio and video calls. Since 2020, Signal has also supported end-to-encrypted video calls. In 2024, Discord added end-to-end encryption for audio and video calls, voice channels, and certain live streams. However, they had no plans to implement E2EE for messages. In 2020, after acquiring Keybase, Zoom announced end-to-end encryption would be limited to paid accounts. Following criticism from human rights advocates, Zoom extended the feature to all users with accounts. In 2021, Zoom settled an $85M class action lawsuit over past misrepresentation about end-to-end encryption. The FTC confirmed Zoom previously retained access to meeting keys. === Other uses === Some encrypted backup and file sharing services provide client-side encryption. Nextcloud and MEGA, offer end-to-end encryption of shared files. The term "end-to-end encryption" is sometimes incorrectly used to describe client-side encryption. Some non-E2EE systems, such as Lavabit and Hushmail, have described themselves as offering "end-to-end" encryption when they did not. == Law enforcement and regulation == In 2022, Facebook Messenger came under scrutiny because the messages between a mother and daughter in Nebraska were used to seek criminal charges in an abortion-rel