Privacy-preserving anonymization of set-valued data

Abstract

In this paper we study the problem of protecting privacy in the publication of set-valued data. Consider a collection of transactional data that contains detailed information about items bought together by individuals. Even after removing all personal characteristics of the buyer, which can serve as links to his identity, the publication of such data is still subject to privacy attacks from adversaries who have partial knowledge about the set. Unlike most previous works, we do not distinguish data as sensitive and non-sensitive, but we consider them both as potential quasi-identifiers and potential sensitive data, depending on the point of view of the adversary. We define a new version of the k-anonymity guarantee, the k m -anonymity, to limit the effects of the data dimensionality and we propose efficient algorithms to transform the database. Our anonymization model relies on generalization instead of suppression, which is the most common practice in related works on such data. We develop an algorithm which finds the optimal solution, however, at a high cost which makes it inapplicable for large, realistic problems. Then, we propose two greedy heuristics, which scale much better and in most of the cases find a solution close to the optimal. The proposed algorithms are experimentally evaluated using real datasets.

An Efficient Technique to Secure Data Access for Multiple Domains using Overlapping Slicing

“…I.Dinur [7] proposed another technique of revealing information while preserving privacy. The authors [6] examine the tradeoff between privacy and usability of statistical databases. D.J.…”

ρ-uncertainty Anonymization by Partial Suppression

“…The original set-valued data privacy problem was defined in the context of association rule hiding [1,15,16], in which the data publisher wishes to "sanitize" the set-valued data (or micro-data) so that all sensitive or "bad" associate rules cannot be discovered while all (or most) "good" rules remain in the published data. Subsequently, a number of privacy models including (h, k, p)-coherence [18], k m -anonymity [14], k-anonymity [9] and ρ-uncertainty [3] have been proposed. k m -anonymity and k-anonymity are carried over directly from relational data privacy, while (h, k, p)-coherence and ρ-uncertainty protect the privacy by bounding the confidence and the support of any sensitive association rule inferrable from the data.…”

“…These generally fall in four categories: global/local generalization [14,9,3], global suppression [18,3], permutation [8] and perturbation [19,4]. Next we briefly discuss the pros and cons of these anonymization techniques.…”

Secure Access to High-dimensional Data through Slicing using Grouping Algorithm

“…Terrovitis et al [10] proposed the km-anonymity model which requires that, for less items or any set of m, the published database contains at least k transactions containing this set of items. This model objects at protecting the database in contradiction of an opponent who has knowledge of at most m items in a exact transaction.…”