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dc.contributor.authorBagai, Rajiv
dc.contributor.authorMalik, Nafia
dc.contributor.authorJadliwala, Murtuza Shabbir
dc.identifier.citationR. Bagai, N. Malik and M. Jadliwala, "Measuring Anonymity of Pseudonymized Data After Probabilistic Background Attacks," in IEEE Transactions on Information Forensics and Security, vol. 12, no. 5, pp. 1156-1169, May 2017en_US
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractThere is clear demand among organizations for sharing their data for mining and other purposes without compromising the privacy of individual objects contained in the data. Pseudonymization is a simple, yet widely employed technique for sanitizing such data prior to its release; it replaces identifying names in the data by pseudonyms. Well-known metrics already exist in the literature for measuring the amount of anonymity still contained in some pseudonymized data in the aftermath of an infeasibility background attack. While the need for a metric for the much wider and more realistic class of probabilistic background attacks has also been well identified, currently no such metric exists. We fulfill that long identified need by presenting two metrics, an approximate and a more exact one, for measuring anonymity in pseudonymized data in the wake of a probabilistic attack. These metrics are rather intractable, thus impractical to employ in real-life situations. Therefore, we also develop an efficient heuristic for our superior metric, and show the remarkable accuracy of our heuristic. Our metrics and heuristic assist a data owner in evaluating the safety level of pseudonymized data against probabilistic attacks before making a decision on its release.en_US
dc.relation.ispartofseriesIEEE Transactions on Information Forensics and Security;v.12:no.5
dc.subjectDegree of anonymityen_US
dc.subjectInfeasibility attacksen_US
dc.subjectProbabilistic attacksen_US
dc.subjectCombinatorial matrix theoryen_US
dc.subjectForecasting errorsen_US
dc.titleMeasuring anonymity of pseudonymized data after probabilistic background attacksen_US
dc.rights.holder© Copyright 2017 IEEE - All rights reserved.en_US

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