Association Rule Hiding Using Chemical Reaction Optimization N. P. Gopalan and T. Satyanarayana Murthy Abstract In recent days, enormous data are generated from departmental stores, hospitals, social media, banks, etc. These datasets are associated with different asso- ciation rules for monitoring the business operations. During this process, to avoid leaking of sensitive information leads to development of association rule hiding algorithms. Many heuristic algorithms are developed but they are limited to optimal solutions. In this paper, an efficient meta-heuristic algorithm has been developed for association rule hiding based on chemical reaction optimization algorithm. The results of the proposed approach are compared with the genetic algorithm, particle swarm optimization, and cuckoo-based algorithms. The experimental results of the proposed algorithm are tested on the benchmark datasets. Keywords Association rules · Association rule hiding · Sensitive data Chemical reaction optimization 1 Introduction Huge amount of data collected from the hospitals, banks, and social media may contain sensitive information in the form of sensitive rules. These are extracted by association rule mining technique in data mining [1–3] which may lead to hiding the sensitive information. The hiding of sensitive information without leaking the business secrets leads to rule hiding. The main goal is to preserve the disclosure of the sensitive association rules. Many approaches are developed for association rule hiding like heuristic, border, reconstruction, and exact-based algorithms. Currently, meta-heuristic algorithms play vital role in hiding the sensitive association rules but limit to optimal results. In this paper, an efficient meta-heuristic algorithm has been N. P. Gopalan · T. S. Murthy (B ) Department of Computer Applications, National Institute of Technology, Trichy 620015, India e-mail: murthyteki@gmail.com N. P. Gopalan e-mail: npgopalan@nitt.edu © Springer Nature Singapore Pte Ltd. 2019 J. C. Bansal et al. (eds.), Soft Computing for Problem Solving, Advances in Intelligent Systems and Computing 816, https://doi.org/10.1007/978-981-13-1592-3_19 249