The concept of privacy-preservation for sensitive data can require the enforcement of privacy policies or the protection against identity disclosure by satisfying some privacy requirements. In this abstract, we investigate privacy-preservation from the anonymity aspect. The sensitive information, even after the removal of identifying attributes, is still susceptible to linking attacks by the authorized users. This problem has been studied extensively in the area of micro data publishing and privacy definitions, e.g., k-anonymity, l-diversity, and variance diversity. In this abstract we formulate the accuracy and privacy constraints as the problem of k-anonymous Partitioning with Imprecision Bounds (k-PIB) and give hardness results. Second, we introduce the concept of accuracy-constrained privacy-preserving access control for relational data. Third, we propose heuristics to approximate the solution of the k-PIB problem and conduct empirical evaluation.
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