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k-anonymity is a property possessed by certain anonymized data. The term k-anonymity was first introduced by Pierangela Samarati and Latanya Sweeney in a paper published in 1998, [1] although the concept dates to a 1986 paper by Tore Dalenius. [2]
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 ...
The l-diversity model is an extension of the 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 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 ...
Exploratory data analysis is an analysis technique to analyze and investigate the data set and summarize the main characteristics of the dataset. Main advantage of EDA is providing the data visualization of data after conducting the analysis.
The SIDE model provides an alternative explanation for effects of anonymity and other "deindividuating" factors that classic deindividuation theory [1] [2] cannot adequately explain. The model suggests that anonymity changes the relative salience of personal vs. social identity, and thereby can have a profound effect on group behavior.
This is particularly used to measure that fraction of income accruing to top earners – top 10%, 1%, 0.1%, 0.01%, and also "top 100" earners or the like; in the US top 400 earners is 0.0002% of earners (2 in 1,000,000) – to study concentration of income – wealth condensation, or rather income condensation. For example, in the chart at ...
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.