Data Anonymisation
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Data Anonymisation
Data anonymization is a type of information sanitization whose intent is privacy protection. It is the process of removing personally identifiable information from data sets, so that the people whom the data describe remain anonymous. Overview Data anonymization has been defined as a "process by which personal data is altered in such a way that a data subject can no longer be identified directly or indirectly, either by the data controller alone or in collaboration with any other party." Data anonymization may enable the transfer of information across a boundary, such as between two departments within an agency or between two agencies, while reducing the risk of unintended disclosure, and in certain environments in a manner that enables evaluation and analytics post-anonymization. In the context of medical data, anonymized data refers to data from which the patient cannot be identified by the recipient of the information. The name, address, and full postcode must be removed ...
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Sanitization (classified Information)
Sanitization is the process of removing sensitive information from a document or other message (or sometimes encrypting it), so that the document may be distributed to a broader audience. When the intent is secrecy protection, such as in dealing with classified information, sanitization attempts to reduce the document's classification level, possibly yielding an unclassified document. When the intent is privacy protection, it is often called data anonymization. Originally, the term sanitization was applied to printed documents; it has since been extended to apply to computer files and the problem of data remanence. Redaction in its sanitization sense (as distinguished from its other editing sense) is the blacking out or deletion of text in a document or the result of doing so. It is intended to allow the selective disclosure of information in a document while keeping other parts of the document secret. Typically the result is a document that is suitable for publication or for diss ...
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Anonymity
Anonymity describes situations where the acting person's identity is unknown. Some writers have argued that namelessness, though technically correct, does not capture what is more centrally at stake in contexts of anonymity. The important idea here is that a person be non-identifiable, unreachable, or untrackable. Anonymity is seen as a technique, or a way of realizing, a certain other values, such as privacy, or liberty. Over the past few years, anonymity tools used on the dark web by criminals and malicious users have drastically altered the ability of law enforcement to use conventional surveillance techniques. An important example for anonymity being not only protected, but enforced by law is the vote in free elections. In many other situations (like conversation between strangers, buying some product or service in a shop), anonymity is traditionally accepted as natural. There are also various situations in which a person might choose to withhold their identity. Acts of cha ...
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Information Privacy
Information privacy is the relationship between the collection and dissemination of data, technology, the public expectation of privacy, contextual information norms, and the legal and political issues surrounding them. It is also known as data privacy or data protection. Data privacy is challenging since attempts to use data while protecting an individual's privacy preferences and personally identifiable information. The fields of computer security, data security, and information security all design and use software, hardware, and human resources to address this issue. Authorities Laws Authorities by country Information types Various types of personal information often come under privacy concerns. Cable television This describes the ability to control what information one reveals about oneself over cable television, and who can access that information. For example, third parties can track IP TV programs someone has watched at any given time. "The addition of any informati ...
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Khaled El Emam
Khaled El Emam is a co-founder and Director at Replica Analytics. El Emam is also a senior scientist at the Children's Hospital of Eastern Ontario ( CHEO) Research Institute and director of the multi-disciplinary Electronic Health Information Laboratory, conducting academic research on de-identification and re-identification risk. As of 2022, El-Emam has served as Editor-in-Chief of JMIR AI, a journal focused on research and applications for the health AI community. El Emam is a Privacy by Design Ambassador recognized by the Ontario Information and Privacy Commissioner. He previously held the Canada Research Chair in Electronic Health Information at the University of Ottawa and was an associate professor in the Faculty of Medicine at the university. He has a PhD from the Department of Electrical and Electronics Engineering, King's College, at the University of London The University of London (UoL; abbreviated as Lond or more rarely Londin in post-nominals) is a federal public ...
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Pseudonymization
Pseudonymization is a data management and de-identification procedure by which personally identifiable information fields within a data record are replaced by one or more artificial identifiers, or pseudonyms. A single pseudonym for each replaced field or collection of replaced fields makes the data record less identifiable while remaining suitable for data analysis and data processing. Pseudonymization (or pseudonymisation, the spelling under European guidelines) is one way to comply with the European Union's new General Data Protection Regulation (GDPR) demands for secure data storage of personal information. Pseudonymized data can be restored to its original state with the addition of information which allows individuals to be re-identified. In contrast, anonymization is intended to prevent re-identification of individuals within the dataset. Impact of Schrems II Ruling The European Data Protection Supervisor (EDPS) on 9 December 2021 highlighted pseudonymization as the t ...
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Statistical Disclosure Control
Statistical disclosure control (SDC), also known as statistical disclosure limitation (SDL) or disclosure avoidance, is a technique used in data-driven research to ensure no person or organization is identifiable from the results of an analysis of survey or administrative data, or in the release of microdata. The purpose of SDC is to protect the confidentiality of the respondents and subjects of the research. SDC usually refers to 'output SDC'; ensuring that, for example, a published table or graph does not disclosure confidential information about respondents. SDC can also describes protection methods applied to the data: for example, removing names and addresses, limiting extreme values, or swapping problematic observations. This is sometimes referred to as 'input SDC', but is more commonly called anonymization, de-identification, or microdata protection. Textbooks (eg ) typically cover input SDC and tabular data protection (but not other parts of output SDC). This is because the ...
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Masking And Unmasking By Intelligence Agencies
Unmasking by U.S. intelligence agencies typically occurs after the United States conducts eavesdropping or other intelligence gathering aimed at foreigners or foreign agents, and the name of a U.S. citizen or entity is incidentally collected. Intelligence reports are then disseminated within the U.S. government, with such names masked to protect those U.S. citizens from invasion of privacy. The names can subsequently be unmasked upon request by authorized U.S. government officials under certain circumstances. Unmaskings occur thousands of times each year, totaling 10,012 in 2019. Jargon When an intelligence agency spies on foreign citizens or agents, and information about innocent domestic citizens is uncovered even though they are not targets of investigation, that is called "incidental collection". If the intelligence agency is operating in a manner designed to protect privacy rights, then it normally addresses incidental collection by using a process called "minimization" whic ...
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L-diversity
''l''-diversity, also written as ''ℓ''-diversity, is a form of group based anonymization that is used to preserve privacy in data sets by reducing the granularity of a data representation. This reduction is a trade off that results in some loss of effectiveness of data management or Data mining, mining algorithms in order to gain some privacy. The ''l''-diversity model is an extension of the k-anonymity, ''k''-anonymity model which reduces the granularity of data representation using techniques including generalization and suppression such that any given record maps onto at least ''k-1'' other records in the data. The ''l''-diversity model handles some of the weaknesses in the ''k''-anonymity model where protected identities to the level of ''k''-individuals is not equivalent to protecting the corresponding sensitive values that were generalized or suppressed, especially when the sensitive values within a group exhibit homogeneity. The ''l''-diversity model adds the promotion of in ...
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K-anonymity
''k''-anonymity is a property possessed by certain anonymized data. The concept of ''k''-anonymity was first introduced by Latanya Sweeney and Pierangela Samarati in a paper published in 1998 as an attempt to solve the problem: "Given person-specific field-structured data, produce a release of the data with scientific guarantees that the individuals who are the subjects of the data cannot be re-identified while the data remain practically useful." A release of data is said to have the ''k''-anonymity property if the information for each person contained in the release cannot be distinguished from at least k - 1 individuals whose information also appear in the release. Unfortunately, the guarantees provided by k-anonymity are aspirational, not mathematical. Methods for ''k''-anonymization To use k-anonymity to process a dataset so that it can be released with privacy protection, a data scientist must first examine the dataset and decide if each attribute (column) is an ''identifie ...
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Geo-Blocking
Geo-blocking or geoblocking is technology that Internet filter, restricts access to Internet content based upon the user's geographical location. In a geo-blocking scheme, the user's location is determined using Internet geolocation techniques, such as checking the user's IP address against a blacklist or whitelist, Global Positioning System, GPS queries in the case of a mobile device, accounts, and measuring the end-to-end delay of a network connection to estimate the physical location of the user. The result of this check is used to determine whether the system will approve or deny access to the website or to particular content. The geolocation may also be used to modify the content provided, for example, the currency in which goods are quoted, the price or the range of goods that are available, besides other aspects. The term is most commonly associated with its use to restrict access to premium multimedia content on the Internet, such as films and television shows, primarily fo ...
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Fillet (redaction)
To fillet in the sense of literary editing is a form of censorship or redaction effected by "cutting out" central letters of a word or name, as if the skeleton of a fish, and replacing them with dashes, to prevent full disclosure (e.g. ' for " William Pitt"). It was frequently practiced in publications of the 18th century in England. Its purpose was to inform interested readers in an obfuscated manner whilst at the same time avoiding the risk of being sued for illegal publication or defamation or libel by the overt naming of persons as having committed certain acts or spoken certain words. It was used for example in parliamentary reports published in ''The Gentleman's Magazine'' from 1738 onwards under the title of the "Debates in the Senate of Magna Lilliputia" in which in order to circumvent the prohibition of the publication of parliamentary debates of the English Parliament the real names of the various orators were filleted or replaced by pseudonym A pseudonym (; ) or a ...
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Differential Privacy
Differential privacy (DP) is a system for publicly sharing information about a dataset by describing the patterns of groups within the dataset while withholding information about individuals in the dataset. The idea behind differential privacy is that if the effect of making an arbitrary single substitution in the database is small enough, the query result cannot be used to infer much about any single individual, and therefore provides privacy. Another way to describe differential privacy is as a constraint on the algorithms used to publish aggregate information about a statistical database which limits the disclosure of private information of records whose information is in the database. For example, differentially private algorithms are used by some government agencies to publish demographic information or other statistical aggregates while ensuring confidentiality of survey responses, and by companies to collect information about user behavior while controlling what is visible ev ...
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