Accuracy-Constrained Privacy-Preserving Access Control Mechanism for Relational Data (International Journal of Advanced Research in Science, Management and Technology, 2017)
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 user can still compromise the privacy of a person leading to identity disclosure. The technique of k-anonymization has been proposed in the literature as an alternative way to release public information, while ensuring both data privacy and data integrity. We prove that two general versions of optimal k-anonymization of relations are NP-hard, including the suppression version which amounts to choosing a minimum number of entries to delete from the relation. The techniques for workload-aware anonymization for selection predicates have been discussed in the literature. 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.
Keywords: Access Control, K-annonymity, L-diversity, Query Imprecision, privacy, Data Confidentiality.