Abstract
Data sanitization process transforms the original database into a modified database to protect the disclosure of sensitive knowledge by reducing the confidence/support of patterns. This process produces side-effects on the sanitized database, where some non-sensitive patterns are lost or new patterns are produced. Recently, a number of approaches have been proposed to minimize these side-effects by selecting appropriate transactions/items for sanitization. The heuristic approach is applied to hide sensitive patterns both in association rules and in frequent itemsets. On the other hand, the border, exact, and evolutionary approaches have only been designed to hide frequent itemsets. In this paper, a new hybrid algorithm, called Decrease the Confidence of Rule (DCR), proposed to improve a border-based solution, namely MaxMin, using two heuristics to hide the association rules. To achieve this, first, a heuristic was formulated in combination with MaxMin solution to select victim items in order to control the impact of sanitization process on result quality. Then, the victim items were removed from transactions with the shortest length. Some experiments have been conducted on the four real datasets to compare performance of DCR with the Association Rule Hiding based on Intersection Lattice (ARHIL) algorithm. The experimental results showed that the proposed algorithm yielded fewer side-effects than ARHIL algorithm. In addition, its efficiency was better than the heuristic approach.





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Bertino E, Fovino IN, Provenza LP (2005) A framework for evaluating privacy preserving data mining algorithms. Data Mining Knowl Discov 11(2):121–154
Amiri A (2007) Dare to share: Protecting sensitive knowledge with data sanitization. Decis Support Syst 43 (1):181–191
Sun X, Yu PS (2005) A border-based approach for hiding sensitive frequent itemsets. In: Proceedings of the 5th IEEE international conference on data mining, pp 426–433
Moustakides GV, Verykios VS (2008) A Max-Min approach for hiding frequent itemsets. Data Knowl Eng 65:75–89
Menon S, Sarkar S, Mukherjee S (2005) Maximizing accuracy of shared databases when concealing sensitive patterns. Inf Syst Res 16(3):256–270
Divanis AG, Verykios V (2006) An integer programming approach for frequent itemset hiding. In: Proceedings of the 15th ACM conference on information and knowledge management, pp 5– 11
Lin CW, Hong TP, Wong JW, Lan GC, Lin WY (2014) A GA-based approach to hide sensitive high utility itemsets. Scientific World Journal
Lin C W, Zhang B, Yang KT, Hong TP (2014) Efficiently hiding sensitive itemsets with transaction deletion based on genetic algorithms. Scientific World Journal
Lin C W, Hong T P, Yang K T, Wang S L (2015) The GA-based algorithms for optimizing hiding sensitive itemsets through transaction deletion. Appl Intell 42(2):210–230
Lin C W, Liu Q, Fournier-Viger P, Hong T P, Voznak M, Zhan J (2016) A sanitization approach for hiding sensitive itemsets based on particle swarm optimization. Eng Appl Artif Intell 53:1–18
Dasseni E, Verykios VS, Elmagarmid AK, Bertino E (2001) Hiding association rules by using confidence and support. In: Proceedings of the 4th information hiding workshop, pp 369–383
Oliveira S R M, Zaiane O R (2002) Privacy preserving frequent itemset mining. In: Proceedings of the IEEE ICDM workshop on privacy, security and data mining 14, pp 43–54
Oliveira SRM, Zaiane OR (2003) Protecting sensitive knowledge by data sanitization. In: Proceedings of the 3rd IEEE international conference on data mining, pp 613–616
Oliveira SRM, Zaiane OR (2003) Algorithms for balancing privacy and knowledge discovery in association rule mining. In: Proceedings of the 7th international database engineering and applications symposium, pp 54–63
Pontikakis ED, Tsitsonis AA, Verykios VS (2004) An experimental study of distortion-based techniques for association rule hiding. In: Proceedings of the 18th conference on database security, pp 325–339
Wang SL, Jafari A (2005) Using unknowns for hiding sensitive predictive association rules. In: Proceedings of the international conference on information reuse and integration, pp 223–228
Wang S L (2009) Maintenance of sanitizing informative association rules. Expert Syst Appl 36:4006–4012
Wang S L, Parikh B, Jafari A (2007) Hiding informative association rule sets. Expert Syst Appl 33 (2):316–323
Wang S L, Maskey R, Jafari A, Hong T P (2008) Efficient sanitization of informative association rules. Expert Syst Appl 35(1-2):442–450
Wang S L, Patel D, Jafari A, Hong T P (2007) Hiding collaborative recommendation association rules. Appl Intell 26(1):66–77
Hai LQ, Somjit A (2012) A conceptual framework for privacy preserving of association rule mining in e-commerce.In: Proceedings of the 7th conference on industrial electronics and applications, pp 1999–2003
Hai L Q, Somjit A, Huy X N, Ngamnij A (2013) Association rule hiding in risk management for retail supply chain collaboration. Comput Indust 64:776–784
Hai LQ, Somjit A, Ngamnij A (2012) Association rule hiding based on distance and intersection lattice. In: Proceedings of the 4th international conference on computer technology and development, pp 227–231
Hai L Q, Somjit A, Ngamnij A (2013) Association rule hiding based on intersection lattice. Mathematical Problems in Engineering
Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of the conference on management of data, pp 207–216
Lin C W, Gan W, Fournier-Viger P, Hong T P, Tseng V S (2016) Efficient algorithms for mining high-utility itemsets in uncertain databases. Knowl-Based Syst 96:171–187
Verykios V S, Divanis A G (2008) A survey of association rule hiding methods for privacy. In: Aggarwal C, Yu P S (eds) Privacy-Preserving Data Mining: Models and Algorithms. Springer, New York, pp 267–289
Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of the20th international conference on very large databases, pp 487–499
Han J, Pei J, Yin Y (2000) Mining frequent pattern without candidate generation. In: Proceedings of the international conference on Management of Data, pp 1–12
Zaki M, Parthasarathy S, Ogihara M, Li W (1997) New algorithms for fast discovery of association rules. The international conference Knowledge Discovery and Data Mining, pp 283– 286
Li J, Choudhary A, Jiang N, Liao W-K (2006) Mining frequent patterns by differential refinement of clustered bitmaps. In: Proceedings of the international conference on data mining, pp 294– 305
Telikani A, Shahbahrami A, Tavoli R (2015) Data sanitization in association rule mining based-on impact factor. Artificial Intelligence and Data Mining, pp 131–140
Atallah M, Bertino E, Elmagarmid A K, Ibrahim M, Verykios VS (1999) Disclosure limitation of sensitive rules. In: Proceedings of the IEEE knowledge and data engineering exchange workshop
Li Y C, Yeh J S, Chang C C (2007) MICF: an effective sanitization algorithm for hiding sensitive patterns on data mining. Adv Eng Inf 21:269–280
Grätzer G (2011) Lattice theory: foundation. Springer, 3rd Edition
Huang H, Wu X, Relue R (2002) Association analysis with one scan of databases. In: Proceedings of the IEEE international conference on data mining, pp 629–632
Goethals B, Zaki M (2004) Advances in frequent itemset mining implementations: Report on FIMI. SIGKDD Explor 6(1):109–117
Bayardo R (1998) Efficiently mining long patterns from databases. In: Proceedings of the conference on management of data, pp 85–93
Kohavi R, Brodley C, Frasca L, Mason L, Zheng Z (2000) KDDCup 2000 organizers’ report: Peeling the onion. SIGKDD Explor 2(2):86–98
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Telikani, A., Shahbahrami, A. Optimizing association rule hiding using combination of border and heuristic approaches. Appl Intell 47, 544–557 (2017). https://doi.org/10.1007/s10489-017-0906-3
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DOI: https://doi.org/10.1007/s10489-017-0906-3