Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Data mining (Invited talk. Abstract only): crossing the Chasm
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Machine Learning
Data Swapping: Balancing Privacy against Precision in Mining for Logic Rules
DaWaK '99 Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery
Selective partial access to a database
ACM '76 Proceedings of the 1976 annual conference
Privacy preserving mining of association rules
Information Systems - Knowledge discovery and data mining (KDD 2002)
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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Data mining, with its objective to efficiently discover valuable and inherent information from large databases, is particularly sensitive to misuse. Therefore an interesting new direction for data mining research is the development of techniques that incorporate privacy concerns and to develop accurate models without access to precise information in individual data records. The difficulty lies in the fact that the two metrics for evaluating privacy preserving data mining methods: privacy and accuracy are typically contradictory in nature. We address privacy preserving mining on distributed data in this paper and present an algorithm, based on the combination of probabilistic approach and cryptographic approach, to protect high privacy of individual information and at the same time acquire a high level of accuracy in the mining result.