Performance tradeoffs for client-server query processing
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Journal of the ACM (JACM)
The state of the art in distributed query processing
ACM Computing Surveys (CSUR)
RANDOM '98 Proceedings of the Second International Workshop on Randomization and Approximation Techniques in Computer Science
k-anonymity: a model for protecting privacy
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
Decentralized Trust Management
SP '96 Proceedings of the 1996 IEEE Symposium on Security and Privacy
Declarative networking: language, execution and optimization
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
Evaluating a Peer-to-Peer Database Server Based on BitTorrent
BNCOD 26 Proceedings of the 26th British National Conference on Databases: Dataspace: The Final Frontier
Data protection in outsourcing scenarios: issues and directions
ASIACCS '10 Proceedings of the 5th ACM Symposium on Information, Computer and Communications Security
Differential privacy under continual observation
Proceedings of the forty-second ACM symposium on Theory of computing
Keep a few: outsourcing data while maintaining confidentiality
ESORICS'09 Proceedings of the 14th European conference on Research in computer security
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
ESORICS'11 Proceedings of the 16th European conference on Research in computer security
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Historically, privacy and efficiency have largely been at odds with one another when querying remote data sources: traditional query optimization techniques provide efficient retrieval by exporting information about the intension of a query to data sources, while private information retrieval (PIR) schemes hide query intension at the cost of extreme computational or communication overheads. Given the increasing use of Internet-scale distributed databases, exploring the spectrum between these two extremes is worthwhile. In this paper, we explore the degree to which query intension is leaked to remote data sources when a variety of existing query processing and view materialization techniques are used. We show that these information flows can be quantified in a concrete manner, and investigate the notion of privacy-aware distributed query evaluation. We then propose two techniques to improve the balance between privacy and efficiency when processing distributed queries, and discuss a number of interesting directions for future work.