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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 whether each attribute (column) is an identifier (identifying), a non-identifier (not-identifying), or a quasi-identifier (somewhat identifying).
Datafly algorithm is an algorithm for providing anonymity in medical data. The algorithm was developed by Latanya Arvette Sweeney in 1997−98. [1] [2] Anonymization is achieved by automatically generalizing, substituting, inserting, and removing information as appropriate without losing many of the details found within the data.
Medical dataset de-anonymization [ edit ] In 1998 Sweeney published a now famous example about data de-anonymization, demonstrating that a medical dataset that was in the public domain, can be used to identify individuals, regardless the removal of all explicit identifiers, when the medical dataset was combined with a public voter list.
According to the EDPS and AEPD, no one, including the data controller, should be able to re-identify data subjects in a properly anonymized dataset. [8] Research by data scientists at Imperial College in London and UCLouvain in Belgium, [ 9 ] as well as a ruling by Judge Michal Agmon-Gonen of the Tel Aviv District Court, [ 10 ] highlight the ...
Data re-identification or de-anonymization is the practice of matching anonymous data (also known as de-identified data) with publicly available information, or auxiliary data, in order to discover the person to whom the data belongs. [1]
Spatial cloaking is a privacy mechanism that is used to satisfy specific privacy requirements by blurring users’ exact locations into cloaked regions. [1] [2] This technique is usually integrated into applications in various environments to minimize the disclosure of private information when users request location-based service.
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.
An example of application of pseudonymization procedure is creation of datasets for de-identification research by replacing identifying words with words from the same category (e.g. replacing a name with a random name from the names dictionary), [11] [12] [13] however, in this case it is in general not possible to track data back to its origins.