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k-Concealment: An Alternative Model of k-Type Anonymity
Tamir Tassa(a),(*), Arnon Mazza(b), Aristides Gionis(c)
Transactions on Data Privacy 5:1 (2012) 189 - 222
Abstract, PDF
(a) Department of Mathematics and Computer Science; The Open University; Ra'anana; Israel.
(b) Department of Mathematics and Computer Science; The Open University; Ra'anana; Israel.
(c) Yahoo! Research; Barcelona; Catalunya; Spain.
e-mail:tamirta @openu.ac.il; ;
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Abstract
We introduce a new model of k-type anonymity, called k-concealment, as an alternative to the well-known model of k-anonymity. This new model achieves similar privacy goals as k-anonymity: While in k-anonymity one generalizes the table records so that each one of them becomes equal to at least k-1 other records, when projected on the subset of quasi-identifiers, k-concealment proposes to generalize the table records so that each one of them becomes computationally - indistinguishable from at least k-1 others. As the new model extends that of k-anonymity, it offers higher utility. To motivate the new model and to lay the ground for its introduction, we first present three other models, called (1, k)-, (k, 1)- and (k, k)-anonymity which also extend k-anonymity. We characterize the interrelation between the four models and propose algorithms for anonymizing data according to them. Since k-anonymity, on its own, is insecure, as it may allow adversaries to learn the sensitive information of some individuals, it must be enhanced by a security measure such as p-sensitivity or l-diversity. We show how also k-concealment can be enhanced by such measures. We demonstrate the usefulness of our models and algorithms through extensive experiments.
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