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
IEEE Computational Science & Engineering
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
A survey on wavelet applications in data mining
ACM SIGKDD Explorations Newsletter
Information sharing across private databases
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Data distortion for privacy protection in a terrorist analysis system
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
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Data mining is the process of automatically searching large amount of data to extract useful information and patterns using tools such as classification, association and rule mining. Data mining often involves data that contains private information such as healthcare or financial records and there has been growing concern about the chance of misusing the personal information extracted from such data. In particular, the increasing ability to trace and collect large amount of data with the use of current technology has led to an interest in the development of data mining algorithms which preserve user privacy. Data perturbation is one of the well known techniques for privacy preserving data mining. In this paper, we investigate the use of the Discrete Wavelet Transform (DWT) with truncation for data perturbation. Our experimental results show that the proposed method is effective in concealing the sensitive information while preserving the performance of data mining techniques after the data distortion.