IMPROVEMENT OF CUCKOO ALGORITHM FOR ASSOCIATION RULE HIDING PROBLEM
Keywords:Cuckoo optimization algorithm, Privacy-preserving data mining, Sensitive association rule hiding, Side effect.
AbstractNowadays, the problem of data security in the process of data mining receives more attention. The question is how to balance between exploiting legal data and avoiding revealing sensitive information. There have been many approaches, and one remarkable approach is privacy preservation in association rule mining to hide sensitive rules. Recently, a meta-heuristic algorithm is relatively effective for this purpose, which is cuckoo optimization algorithm (COA4ARH). In this paper, an improved version of COA4ARH is presented for calculating the minimum number of sensitive items which should be removed to hide sensitive rules, as well as limit the loss of non-sensitive rules. The experimental results gained from three real datasets showed that the proposed method has better results compared to the original algorithm in several cases.
Agrawal, R., & Srikant, R. (2000). Privacy-preserving data mining. SIGMOD Record, 29(2), 439-450.
Atallah, M., Bertino, E., Elmagarmid, A., Ibrahim, M., & Verykios, V. (1999). Disclosure limitation of sensitive rules. Paper presented at The IEEE Knowledge and Data Engineering Exchange Workshop (KDEX), USA.
Chang, L., & Moskowitz, I. (1998). Parsimonious downgrading and decision trees applied to the inference problem. Paper presented at The Workshop on New Security Paradigms (NSPW), USA.
Lindell, Y., & Pinkas, B. (2000). Privacy-preserving data mining. Journal of Cryptology, 15(3), 36-54.
Mahtab, H. A., Mohammad, N. D., & Mehdi, A. (2016). Association rule hiding using Cuckoo optimization algorithm. Expert Systems with Applications, 64, 340-351.
Oliveira, S., & Zaïane, O. (2004). Achieving privacy preservation when sharing data for clustering. Paper presented at The International Conference on Data Mining (SDM), Canada.
UCI. (2018). Machine learning repository. Retrieved from https://archive.ics.uci.edu/ml/index.php
Walton, S., Hassan, O., Morgan, K., & Brown, M. (2011). Modified Cuckoo search: A new gradient-free optimisation algorithm. Chaos, Solitons & Fractals, 44, 710-718.
Wu, Y. H., Chiang, C. M., & Arbee, L. P. C. (2007). Hiding sensitive association rules with limited side effects. IEEE Transactions on Knowledge and Data Engineering, 19(1), 29-42.
Volume and Issues
Copyright & License
Copyright (c) 2018 Đoàn Minh Khuê, Lê Hoài Bắc
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.