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DISTORTION-BASED HEURISTIC METHOD FOR SENSITIVE ASSOCIATION RULE HIDING

Bac Le, Lien Kieu, Dat Tran

Abstract


In the past few years, privacy issues in data mining have received considerable attention in the data mining literature. However, the problem of data security cannot simply be solved by restricting data collection or against unauthorized access, it should be dealt with by providing solutions that  not only protect sensitive information, but also not affect to the accuracy of the results in data mining and not violate the sensitive knowledge related with individual privacy or competitive advantage in businesses. Sensitive association rule hiding is an important issue in privacy preserving data mining. The aim of association rule hiding is to minimize the side effects on the sanitized database, which means to reduce the number of missing non-sensitive rules and the number of generated ghost rules. Current methods for hiding sensitive rules cause side effects and data loss. In this paper, we introduce a new distortion-based method to hide sensitive rules. This method proposes the determination of critical transactions based on the number of non-sensitive maximal frequent itemsets that contain at least one item to the consequent of the sensitive rule, they can be directly affected by the modified transactions. Using this set, the number of non-sensitive itemsets that need to be considered is reduced dramatically. We compute the smallest number of transactions for modification in advance to minimize the damage to the database. Comparative experimental results on real datasets showed that the proposed method can achieve better results than other methods with fewer side effects and data loss.


Keywords


Privacy preserving data ming; Association rule hiding; Side effects; Distortion-based method

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References


Amiri, A. Dare to share: Protecting sensitive knowledge with data sanitization. Decision Support Systems, 43(1), 181–191(2007).

Atallah, M., Bertino, E., Elmagarmid, A., Ibrahim, M., & Verykios, V.S.: Disclosure limitation of sensitive rules. In: Proceedings of the IEEE knowledge and data engineering exchange workshop (pp. 45–52) (1999).

Bayardo, R.: Efficiently mining long patterns from databases. In: Proceedings of the ACM SIGMOD international conference on management of data (1998).

Cheng, P., Roddick, J. F., Chu, S. C., & Lin, C. W.: Privacy preservation through a greedy, distortion-based rule-hiding method. Applied Intelligence, 44(2), 295–306 (2015).

Dasseni, E., Verykios, V.S., Elmagarmid, A.K., & Bertino, E.: Hiding association rules by using confidence and support. In: Proceedings of the 4th international workshop on information hiding (pp. 369–383) (2001).

Divanis AG, Verykios V: An integer programming approach for frequent itemset hiding.

In: Proceedings of the 15th ACM conference on information and knowledge management, pp 5-11 (2006).

Kohavi CE, Brodley R et al. : Kdd-cup 2000 organizers’ report: Peeling the onion. ACM SIGKDD Explorations Newsletter 2(2):86–93 (2000).

Lin, C.W., Hong, T. P., Wong, J. W., Lan, G.C., & Lin, W.Y.: A GA-based approach to hide sensitive high utility itemsets. Scientific World Journal (2014). doi:10.1155/2014/804629.

Lin, C.W., Zhang, B., Yang, K.T., & Hong, T.P.: Efficiently hiding sensitive itemsets with transaction deletion based on genetic algorithms. Scientific World Journal, 2014. doi:10.1155/2014/398269 (2014).

Lin CW, Hong TP, Yang KT, Wang SL: The GA-based algorithms for optimizing hiding sensitive itemsets through trans-action deletion. Appl Intell 42(2):210–230 (2015).

Menon, S., Sarkar, S., & Mukherjee, S.: Maximizing accuracy of shared databases when concealing sensitive patterns. Information Systems Research, 16(3), 256–270 (2005).

Moustakides, G.V., & Verykios, V.S.: A Max-Min approach for hiding frequent itemsets. Data & Knowledge Engineering, 65, 75–89 (2008).

Oliveira, S.R.M., & Zaiane, O.R.: Privacy preserving frequent itemset mining. In: Proceedings of the IEEE international conference on privacy, security and data mining, pp. 43–54 (2002).

Sun, X., & Yu, P.S.: A border–based approach for hiding sensitive frequent itemsets.

In: Proceedings of the 5th IEEE international conference on datamining, pp. 426–433 (2005).




DOI: https://doi.org/10.15625/1813-9663/35/4/14131 Display counter: Abstract : 16 views. PDF : 9 views.

Journal of Computer Science and Cybernetics ISSN: 1813-9663

Published by Vietnam Academy of Science and Technology