05758559

j

05758559 Matatov, Nissim Rokach, Lior Maimon, Oded Privacy-preserving data mining: a feature set partitioning approach. Inf. Sci. 180, No. 14, 2696-2720 (2010). 2010 Elsevier Science Inc. (North-Holland), New York, NY EN data mining privacy genetic algorithms $k$-anonymity feature set partitioning

doi:10.1016/j.ins.2010.03.011

Summary: In privacy-preserving data mining (PPDM), a widely used method for achieving data mining goals while preserving privacy is based on $k$-anonymity. This method, which protects subject-specific sensitive data by anonymizing it before it is released for data mining, demands that every tuple in the released table should be indistinguishable from no fewer than $k$ subjects. The most common approach for achieving compliance with $k$-anonymity is to replace certain values with less specific but semantically consistent values. In this paper we propose a different approach for achieving $k$-anonymity by partitioning the original dataset into several projections such that each one of them adheres to $k$-anonymity. Moreover, any attempt to rejoin the projections, results in a table that still complies with $k$-anonymity. A classifier is trained on each projection and subsequently, an unlabelled instance is classified by combining the classifications of all classifiers.Guided by classification accuracy and $k$-anonymity constraints, the proposed data mining privacy by decomposition (DMPD) algorithm uses a genetic algorithm to search for optimal feature set partitioning. Ten separate datasets were evaluated with DMPD in order to compare its classification performance with other $k$-anonymity-based methods. The results suggest that DMPD performs better than existing $k$-anonymity-based algorithms and there is no necessity for applying domain dependent knowledge. Using multiobjective optimization methods, we also examine the tradeoff between the two conflicting objectives in PPDM: privacy and predictive performance.