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SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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Optimal and approximate computation of summary statistics for range aggregates
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
On the design and quantification of privacy preserving data mining algorithms
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
STHoles: a multidimensional workload-aware histogram
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
The statistical security of a statistical database
ACM Transactions on Database Systems (TODS)
A study on the protection of statistical data bases
SIGMOD '77 Proceedings of the 1977 ACM SIGMOD international conference on Management of data
Genetic Algorithms in Search, Optimization and Machine Learning
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Proceedings of the 2002 ACM SIGMOD international conference on Management of data
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VLDB '82 Proceedings of the 8th International Conference on Very Large Data Bases
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SP '97 Proceedings of the 1997 IEEE Symposium on Security and Privacy
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OSDI'00 Proceedings of the 4th conference on Symposium on Operating System Design & Implementation - Volume 4
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Journal of Cognitive Neuroscience
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Proceedings of the 2007 ACM symposium on Applied computing
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Data & Knowledge Engineering
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Information Sciences: an International Journal
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VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms
IEEE Transactions on Mobile Computing
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EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
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ACM SIGMOD Record
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Privacy-preserving data analytics as an outsourced service
Proceedings of the 2008 ACM workshop on Secure web services
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Secure multidimensional range queries in sensor networks
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Privacy-Preserving Queries for a DAS Model Using Encrypted Bloom Filter
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Partially materialized digest scheme: an efficient verification method for outsourced databases
The VLDB Journal — The International Journal on Very Large Data Bases
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Transactions on Data Privacy
A distributed approach to enabling privacy-preserving model-based classifier training
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SDM '09 Proceedings of the 6th VLDB Workshop on Secure Data Management
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ICCOMP'09 Proceedings of the WSEAES 13th international conference on Computers
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SSS '09 Proceedings of the 11th International Symposium on Stabilization, Safety, and Security of Distributed Systems
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A cryptography index technology and method to measure information disclosure in the DAS model
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ASIACCS '10 Proceedings of the 5th ACM Symposium on Information, Computer and Communications Security
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Time and space efficient algorithms for two-party authenticated data structures
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A secure multi-dimensional partition based index in DAS
APWeb'08 Proceedings of the 10th Asia-Pacific web conference on Progress in WWW research and development
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Metadata management in outsourced encrypted databases
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Query log perturbation method for privacy preserving query
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CAiSE'06 Proceedings of the 18th international conference on Advanced Information Systems Engineering
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DBSec'05 Proceedings of the 19th annual IFIP WG 11.3 working conference on Data and Applications Security
Privacy-preserving queries on encrypted data
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Secure multidimensional range queries over outsourced data
The VLDB Journal — The International Journal on Very Large Data Bases
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Inference attack against encrypted range queries on outsourced databases
Proceedings of the 4th ACM conference on Data and application security and privacy
Dividing secrets to secure data outsourcing
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Wireless Personal Communications: An International Journal
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Database outsourcing is an emerging data management paradigm which has the potential to transform the IT operations of corporations. In this paper we address privacy threats in database outsourcing scenarios where trust in the service provider is limited. Specifically, we analyze the data partitioning (bucketization) technique and algorithmically develop this technique to build privacy-preserving indices on sensitive attributes of a relational table. Such indices enable an untrusted server to evaluate obfuscated range queries with minimal information leakage. We analyze the worst-case scenario of inference attacks that can potentially lead to breach of privacy (e.g., estimating the value of a data element within a small error margin) and identify statistical measures of data privacy in the context of these attacks. We also investigate precise privacy guarantees of data partitioning which form the basic building blocks of our index. We then develop a model for the fundamental privacy-utility tradeoff and design a novel algorithm for achieving the desired balance between privacy and utility (accuracy of range query evaluation) of the index.