Abstract
In recent days, enormous data are generated from departmental stores, hospitals, social media, banks, etc. These datasets are associated with different association 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.
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Gopalan, N.P., Murthy, T.S. (2019). Association Rule Hiding Using Chemical Reaction Optimization. In: Bansal, J., Das, K., Nagar, A., Deep, K., Ojha, A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 816. Springer, Singapore. https://doi.org/10.1007/978-981-13-1592-3_19
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DOI: https://doi.org/10.1007/978-981-13-1592-3_19
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