The density-based clustering method for privacy-preserving data mining

Abstract

Privacy-preserving data mining has become an interesting and emerging issue in recent years since it can, not only hide the sensitive information but still mine the meaningful knowledge at the same time. Since privacy-preserving data mining is a non-trivial task, which is also concerned as a NP-hard problem, several evolutionary algorithms were presented to find the optimized solutions but most of them focus on considering a single-objective function with the pre-defined weight values of three side effects (hiding failure, missing cost, and artificial cost). In this paper, we aim at designing a multiple objective particle swarm optimization method for hiding the sensitive information based on the density clustering approach (named CMPSO). The presented CMPSO is more flexible to select the most appropriate solutions for hiding the sensitive information based on user's preference. Extensive experiments are carried on two datasets to show that the designed CMPSO algorithm has good performance than the traditional single-objective evolutionary approaches in terms of three side effects.

Keywords

• density clustering
• pareto solutions
• optimization
• ppdm
• deletion