Author
Zahid Pervaiz, Walid G. Aref, Arif Ghafoor, and Nagabhushana Prabhu
Abstract
Access control mechanisms protect sensitive information from unauthorized users. However, when sensitive information is shared and a Privacy Protection Mechanism (PPM) is not in place, an authorized insider can still compromise the privacy of a person leading to identity disclosure. A PPM can use suppression and generalization to anonymize and satisfy privacy requirements, e.g., k-anonymity and l-diversity, against identity and attribute disclosure. However, the protection of privacy is achieved at the cost of precision of authorized information.
In this paper, we propose a 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.
Key alpha
Access Control, Privacy, k-anonymity, Query Evaluation
