Journal of the ACM (JACM)
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Replication is not needed: single database, computationally-private information retrieval
FOCS '97 Proceedings of the 38th Annual Symposium on Foundations of Computer Science
Incognito: efficient full-domain K-anonymity
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Towards Privacy-Aware Location-Based Database Servers
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Private queries in location based services: anonymizers are not necessary
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Preservation of proximity privacy in publishing numerical sensitive data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
A survey of single-database private information retrieval: techniques and applications
PKC'07 Proceedings of the 10th international conference on Practice and theory in public-key cryptography
Secure data management in the cloud
DNIS'11 Proceedings of the 7th international conference on Databases in Networked Information Systems
Secure and privacy-preserving data services in the cloud: a data centric view
Proceedings of the VLDB Endowment
Anonymous spatial query on non-uniform data
Proceedings of the 14th International Conference on Information Integration and Web-based Applications & Services
Towards practical private processing of database queries over public data
Distributed and Parallel Databases
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Private retrieval of public data is useful when a client wants to query a public data service without revealing the query to the server. Computational Private Information Retrieval (cPIR) achieves complete privacy for clients, but is deemed impractical since it involves expensive computation on all the data on the server. Besides, it is inflexible if the server wants to charge the client based on the service data that is exposed. k-Anonymity, on the other hand, is flexible and cheap for anonymizing the querying process, but is vulnerable to privacy and security threats. We propose a practical and flexible approach for the private retrieval of public data called Bounding-Box PIR (bbPIR). Using bbPIR, a client specifies both privacy requirements and a service charge budget. The server satisfies the client's requirements, and achieves overall good performance in computation and communication. bbPIR generalizes cPIR and k-Anonymity in that the bounding box can include as much as all the data on the server or as little as just k data items. The efficiency of bbPIR compared to cPIR and the effectiveness of bbPIR compared to k-Anonymity are verified in extensive experimental evaluations.