Sensitive association rules hiding using electromagnetic field optimization algorithm

Privacy preserving data mining has been a major research subject in recent years. The most important goal of this area is to protect personal information and prevent disclosure of this information during the data mining process. There are various techniques in the field of privacy preserving data mining. One of these techniques is association rules mining. The main purpose of association rules mining is to hide sensitive association rules. So far, various algorithms have been presented to this field in order to reach the purpose of sensitive association rules hiding. Each algorithm has its own specific functions and methods. To hide sensitive association rules, this paper presents an electromagnetic field optimization algorithm (EFO4ARH). This algorithm utilizes the data distortion technique to hide the sensitive association rules. In this algorithm, two fitness functions are used to reach the solution with the least side effects. Also, in this algorithm, the runtime has been reduced. This algorithm consists of a technique for exiting from local optima point and moving toward global optimal points. The performance of the proposed algorithm is evaluated by doing experiments on both real-world and synthetic datasets. Compared to four reference algorithms, the proposed algorithm shows a reduction in the side effects and better preservation of data quality. The performance of EFO4ARH is tested by standard deviation and mean Friedman ranks of error for standard functions (CEC benchmarks). In addition, hiding experiments show that our proposed algorithm outperforms existing hiding algorithms. (C) 2018 Elsevier Ltd. All rights reserved.