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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).
Previous work has suggested that it is therefore difficult to control the anonymity set size when using approximations of the Birthday Paradox. [12] Instead, Ali and Dyo use the overall rate of collision in the dataset and provide that the probability of there being a collision p can be calculated by p = 1 − ( 1 − 1 / n ) m − 1 ...
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 sanitization is an integral step to privacy preserving data mining because private datasets need to be sanitized before they can be utilized by individuals or companies for analysis. The aim of privacy preserving data mining is to ensure that private information cannot be leaked or accessed by attackers and sensitive data is not traceable ...
The Protection of Human Subjects ('Common Rule'), a collection of multiple U.S. federal agencies and departments including the U.S. Department of Health and Human Services, warn that re-identification is becoming gradually easier because of "big data"—the abundance and constant collection and analysis of information along with the evolution ...
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.
QI values are handled following specific standards. For example, the k-anonymization replaces some original data in the records with new range values and keep some values unchanged. New combination of QI values prevents the individual from being identified and also avoid destroying data records.
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.