Privacy Preserving Distributed Data Mining with Evolutionary Computing

Publishing data about individuals without revealing sensitive information about them is an important problem. Distributed data mining applications use sensitive data from distributed databases held by different parties. This comes into direct conflict with an individual's need and right to privacy. It is thus of great importance to develop adequate security techniques for protecting privacy of individual values used for data mining. Here, we study how to maintain privacy in distributed data mining. That is, we study how two (or more) parties can find frequent itemsets in a distributed database without revealing each party's portion of the data to the other. In this paper, we consider privacy-preserving na ve-Bayes classifier for horizontally partitioned distributed data and propose data mining privacy by decomposition (DMPD) method that uses genetic algorithm to search for optimal feature set partitioning by classification accuracy and k-anonymity constraints.