Query evaluation techniques for large databases
ACM Computing Surveys (CSUR)
The multilevel relational (MLR) data model
ACM Transactions on Information and System Security (TISSEC)
Access path selection in a relational database management system
SIGMOD '79 Proceedings of the 1979 ACM SIGMOD international conference on Management of data
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Benchmarking Database Systems A Systematic Approach
VLDB '83 Proceedings of the 9th International Conference on Very Large Data Bases
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Privacy Promises, Access Control, and Privacy Management
ISEC '02 Proceedings of the Third International Symposium on Electronic Commerce
Purpose based access control of complex data for privacy protection
Proceedings of the tenth ACM symposium on Access control models and technologies
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Limiting disclosure in hippocratic databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Enforcing purpose of use via workflows
Proceedings of the 8th ACM workshop on Privacy in the electronic society
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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.