Distributed Data Mining Privacy by Decomposition (DDMPD) with Naive Bayes Classifier and Genetic Algorithm
Lambodar Jena, Narendra Kumar Kamila · 2013
Publishing data about individuals without revealing sensitive information about them is an important problem. Distributed data mining applications, such as those dealing with health care, finance, counter-terrorism and homeland defence, 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 mining of frequent itemsets. 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 naive Bayes classifier for horizontally partitioned distributed data and propose a two-party protocol and a multi-party protocol to achieve it. By classification accuracy and k-anonymity constraints, the proposed data mining privacy by decomposition (DMPD) method uses a genetic algorithm to search for optimal feature set partitioning. Multiobjective optimization methods are used to examine the tradeoff between privacy and predictive performance.