Approximation Algorithms for k-Anonymity

Gagan Aggarwal, Tomas Feder, Krishnaram Kenthapadi, Rajeev Motwani, Rina Panigrahy, Dilys Thomas, and An Zhu, Google, Inc. and Stanford University, Paper Number: 20051120001.

We consider the problem of releasing a table containing personal records, while ensuring individual privacy and maintaining data integrity to the extent possible. One of the techniques proposed in the literature is k-anonymization. A release is considered k-anonymous if the information corresponding to any individual in the release cannot be distinguished from that of at least k - 1 other individuals whose information also appears in the release. In order to achieve k-anonymization, some of the entries of the table are either suppressed or generalized (e.g. an Age value of 23 could be changed to the Age range 20-25). The goal is to lose as little information as possible while ensuring that the release is k-anonymous. This optimization problem is referred to as the k-Anonymity problem. We show that the k-Anonymity problem is NP-hard even when the attribute values are ternary and we are allowed only to suppress entries. On the positive side, we provide an O(k)-approximation algorithm for the problem. We also give improved positive results for the interesting cases with specific values of k — in particular, we give a 1.5-approximation algorithm for the special case of 2-Anonymity, and a 2-approximation algorithm for 3-Anonymity.

Keywords: Anonymity, Approximation Algorithms, Database Privacy

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