Privacy Preservation and Protection by Extending Generalized Partial Indices

Privacy violation has attracted more and more attention from the public, and privacy preservation has become a hot topic in academic communities, industries and societies. Recent research has been focused on purpose-based techniques and models with little consideration on balancing privacy enhancement and performance. We propose an efficient Privacy Aware Partial Index (PAPI) mechanism based on both the concept of purposes and the theory of partial indices. In the PAPI mechanism, all purposes are independent from each other and organized in a flatten purpose tree($\mathcal{FPT}$). Thus, security administrators can update the flatten purpose tree by adding or deleting purposes. Intended purposes are maintained in PAPI directly. Furthermore, based on the PAPI mechanism, we extend the existing query optimizer and executor to enforce the privacy policies. Finally, the experimental results demonstrate the feasibility and efficiency of the PAPI mechanism.