ANONYMITY BASED ACCURACY-CONSTRAINED PRIVACY-PRESERVING ACCESS CONTROL FRAMEWORK FOR RELATIONAL DATA

In this abstract the focus is on a static relational table that is anonymized only once. To exemplify our approach, role-based access control is assumed. However, the concept of accuracy constraints for permissions can be applied to any privacy-preserving security policy, e.g., discretionary access control. The heuristics proposed in this abstract for accuracy-constrained privacy-preserving access control are also relevant in the context of workload-aware anonymization. In this abstract, we propose an accuracy-constrained privacy-preserving access control framework. The access control policies define selection predicates available to roles while the privacy requirement is to satisfy the k-anonymity or l-diversity. An additional constraint that needs to be satisfied by the PPM is the imprecision bound for each selection predicate. The techniques for workload-aware anonymization for selection predicates have been discussed in the literature. However, to the best of our knowledge, the problem of satisfying the accuracy constraints for multiple roles has not been studied before. In our formulation of the aforementioned problem, we propose heuristics for anonymization algorithms and show empirically that the proposed approach satisfies imprecision bounds for more permissions and has lower total imprecision than the current state of the art.