Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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
Privacy preserving mining of association rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Assuring privacy when big brother is watching
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Privacy-preserving k-means clustering over vertically partitioned data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Leveraging the "Multi" in secure multi-party computation
Proceedings of the 2003 ACM workshop on Privacy in the electronic society
Privacy: A Machine Learning View
IEEE Transactions on Knowledge and Data Engineering
When do data mining results violate privacy?
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy preserving sequential pattern mining in distributed databases
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Association rules mining in vertically partitioned databases
Data & Knowledge Engineering - Special issue: WIDM 2004
Secure set intersection cardinality with application to association rule mining
Journal of Computer Security
Privacy-preserving Naïve Bayes classification
The VLDB Journal — The International Journal on Very Large Data Bases
Privacy-preserving decision trees over vertically partitioned data
ACM Transactions on Knowledge Discovery from Data (TKDD)
Information Sciences: an International Journal
SAX: a privacy preserving general pupose methodapplied to detection of intrusions
Proceedings of the ACM first international workshop on Privacy and anonymity for very large databases
Efficient techniques for privacy-preserving sharing of sensitive information
TRUST'11 Proceedings of the 4th international conference on Trust and trustworthy computing
Proceedings of the 10th annual ACM workshop on Privacy in the electronic society
Privacy preserving unsupervised clustering over vertically partitioned data
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part V
Computing join aggregates over private tables
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
A Privacy Preserving Method Using Privacy Enhancing Techniques for Location Based Services
Mobile Networks and Applications
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Due to privacy concerns, clients of some services may not want to reveal their private information. Even in these situations, data mining is feasible without sacrificing user privacy. Prior approaches to this problem generally trade off accuracy for security, without giving provable bounds on security. Alternatives to the randomization technique are required to enable accurate data mining while strictly preserving privacy. In this paper, we present a general architecture that enables privacy-preserving mining of client information. Under some reasonable assumptions, we show that our methods are secure, while maintaining the accuracy of the results.