Reinforced Social Ant with Discrete Swarm Optimizer for Sensitive Item and Rule Hiding

Abstract

In data mining Privacy Preserving Data mining (PPDM) of the important research areas concentrated in recent years which ensures ensuring sensitive information and rule not being revealed. Several methods and techniques were proposed to hide sensitive information and rule in databases. In the past, perturbation-based PPDM was developed to preserve privacy before use and secure mining of association rules were performed in horizontally distributed databases. This paper presents an integrated model for solving the multi-objective factors, data and rule hiding through reinforcement and discrete optimization for data publishing. This is denoted as an integrated Reinforced Social Ant and Discrete Swarm Optimization (RSA-DSO) model. In RSA-DSO model, both Reinforced Social Ant and Discrete Swarm Optimization perform with the same particles. To start with, sensitive data item hiding is performed through Reinforced Social Ant model. Followed by this performance, sensitive rules are identified and further hidden for data publishing using Discrete Swarm Optimization model. In order to evaluate the RSA-DSO model, it was tested on benchmark dataset. The results show that RSA-DSO model is more efficient in improving the privacy preservation accuracy with minimal time for optimal hiding and also optimizing the generation of sensitive rules.