Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A methodology for hiding knowledge in databases
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
Using randomized response techniques for privacy-preserving data mining
Proceedings of the ninth 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
Privacy preserving naive bayes classification
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Mining association rules from distorted data for privacy preservation
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
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Privacy preserving data mining is a novel research direction. The main objective is to develop algorithms for modifying the original data in some way, so that the private information remains private even fter the mining process. Agrawal and Srikant first proposed a scheme for privacy preserving data mining using random perturbation [1]. Then, Rizvi and Haritsa presented a scheme called MASK to mine associations with secrecy constraints [2]; Du and Zhan proposed an approach to conduct privacy preserving decision tree building [3]. A methodology for hiding knowledge in database was also presented and applied to classification and association rule mining [4]. However, all those approaches are different in their frameworks and processes. Only can they deal with a special data type, a given mining algorithm, and one kind of the attribute of private information.