Research On Privacy- Preserving Data Mining Based On K-anonymity Algorithm

With the rapid development of information technology,more and more activities . on data sharing and exchanging are coming forth in the network. Data mining,which is capable of acquiring interesting knowledge from rough data,has become an extensively applied analysis method. 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 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.Consider of classification accuracy and k-anonymity constraints, the proposed data mining privacy by decomposition algorithm uses a genetic algorithm to search for optimal feature set partitioning. We use ten separate datasets to evaluated the DMPD algorithm in order to compare its classification performance with other k-anonymity-based methods. Experiment result show that our method 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.