Association rule hiding methods

Advanced Review

Vassilios S. Verykios

Published Online: Jan 09 2013

DOI: 10.1002/widm.1082

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Abstract Privacy preserving data mining has been recently introduced to cope with privacy issues related to the data subjects in the course of the mining of the data. It has also been recognized that it is not only the data that need to be protected but sensitive knowledge hidden in the data as well. Knowledge hiding is an emerging area of research focusing on appropriately modifying the data in such a way that sensitive knowledge escapes the mining and is not communicated to the public for privacy purposes. This article investigates the development of techniques falling under the knowledge‐hiding umbrella that pertain to the association rule‐mining task. These techniques are known as association rule hiding or frequent pattern hiding approaches, and have been receiving a lot of attention lately because they touch upon important issues of handling a sort of commonly used patterns such as the frequent patterns and the association rules. We present an overview of this area as well as a taxonomy and a presentation of an important sample of algorithms. © 2013 Wiley Periodicals, Inc. This article is categorized under: Algorithmic Development > Association Rules Technologies > Association Rules

References

O`Leary, DE.Knowledge discovery as a threat to database security. In:Proceedings of the 1st International Conference on Knowledge Discovery and Databases;1991,107–516.
Clifton, C,Marks, D.Security and privacy implications of data mining. In:Proceedings of the ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery;1996,15–19.
Atallah, M,Elmagarmid, A,Ibrahim, M,Bertino, E,Verykios, V.Disclosure limitation of sensitive rules. In:Proceedings of the 1999 Workshop on Knowledge and Data Engineering Exchange;1999,45.
Clifton, C.Using sample size to limit exposure to data mining.J Comput Secur2000,8:281–307.
Agrawal, R,Srikant, R.Privacy‐preserving data mining. In:Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data;Dallas, Texas;2000,439–450.
Verykios, VS,Elmagarmid, AK,Bertino, E,Saygin, Y,Dasseni, E.Association rule hiding.IEEE Trans Knowledge Data Eng2004,16:434–447.
Dasseni, E,Verykios, VS,Elmagarmid, AK,Bertino, E.Hiding association rules by using confidence and support. In:Proceedings of the 4th International Workshop on Information Hiding;2001,369–383.
Oliveira, SRM,Zaïane, OR.Privacy preserving frequent itemset mining. In:Proceedings of the IEEE International Conference on Privacy, Security and Data Mining;Maebashi City, Japan;2002,43–54.
Oliveira, SRM,Zaïane, OR.Algorithms for balancing privacy and knowledge discovery in association rule mining. In:Proceedings of the 7th International Database Engineering and Applications Symposium (IDEAS`03);Hong Kong, China;2003,54–63.
Oliveira, SRM,Zaïane, OR.Protecting sensitive knowledge by data sanitization. In:Proceedings of the Third IEEE International Conference on Data Mining;2003,613.
Oliveira, SRM,Zaiane, OR.A unified framework for protecting sensitive association rules in business collaboration.Int J Bus Intell Data Mining2006,1:247–287.
Saygin, Y,Verykios, VS,Elmagarmid, AK.Privacy preserving association rule mining. In:Proceedings of the 12th International Workshop on Research Issues in Data Engineering: Engineering E‐Commerce/E‐Business Systems (RIDE`02);2002,151.
Saygin, Y,Verykios, VS,Clifton, C.Using unknowns to prevent discovery of association rules.ACM SIGMOD Record2001,30:46–54.
Verykios, VS,Pontikakis, ED,Theodoridis, Y,Chang, L.Efficient algorithms for distortion and blocking techniques in association rule hiding.Distributed Parallel Databases2007,22:85–104.
Wu, Y‐H,Chiang, C‐M,Chen, ALP.Hiding sensitive association rules with limited side effects.IEEE Trans Knowledge Data Eng2007,19:29–42.
Sun, X,Yu, PS.A border‐based approach for hiding sensitive frequent itemsets. In:Proceedings of the Fifth IEEE International Conference on Data Mining;2005,426–433.
Sun, X,Yu, PS.Hiding sensitive frequent itemsets by a border‐based approach.JCSE2007,1:74–94.
Mannila, H,Toivonen, H.Levelwise search and borders of theories in knowledge discovery.Data Mining Knowledge Discov1997,1:241–258.
Moustakides, GV,Verykios, VS.A Max‐Min approach for hiding frequent itemsets. In:Proceedings of the Sixth IEEE International Conference on Data Mining–‐Workshops;2006,502–506.
Moustakides, GV,Verykios, VS.A MaxMin approach for hiding frequent itemsets.Data Knowledge Eng2008,65:75–89.
Menon, S,Sarkar, S,Mukherjee, S.Maximizing accuracy of shared databases when concealing sensitive patterns.Inf Syst Res2005,16:256–270.
Menon, S,Sarkar, S.Minimizing information loss and preserving privacy.Manage Sci2007,53:101–116.
Gkoulalas‐Divanis, A,Verykios, VS.An integer programming approach for frequent itemset hiding. In:International Conference on Information and Knowledge Management, Proceedings;2006,748–757.
Gkoulalas‐Divanis, A,Verykios, VS.Hiding sensitive knowledge without side effects.Knowledge Inf Syst2009,20:263–299.
Gkoulalas‐Divanis, A,Verykios, VS.Exact knowledge hiding through database extension.IEEE Trans Knowledge Data Eng2009,21:699–713.
Oliveira, SRM,Zaiane, OR,Saygin, Y.Secure association rule sharing.Lect Notes Artif Intell (Subseries Lect Notes in Comput Sci)2004,3056:74–85.
Wang, Z,Wang, W,Shi, B.Blocking inference channels in frequent pattern sharing (article #4221817). In:Proceedings of the International Conference on Data Engineering;2007,1425–1429.
Kantarcioglu, M,Clifton, C.Privacy‐preserving distributed mining of association rules on horizontally partitioned data.IEEE Trans Knowledge Data Eng2004,16:1026–1037.
Mielikainen, T.Inverse frequent set mining. IEEE ICDM Workshop on Privacy Preserving Data Mining.Melbourne, Florida: IEEE;2003,18–23.
Calders, T.Axiomatization and Deduction Rules for the Frequency of Itemsets. PhD Thesis, Universiteit Antwerpen,2003.
Calders, T.Computational complexity of itemset frequency satisfiability. In:Proceedings of the ACM SIGACT‐SIGMOD‐SIGART Symposium on Principles of Database Systems;2004,23:143–154.
Ramesh, G,Maniatty, WA,Zaki, MJ.Feasible itemset distributions in data mining: theory and application. In:Proceedings of the ACM SIGACT‐SIGMOD‐SIGART Symposium on Principles of Database Systems;2003,22:284–295.
Wang, Y,Wu, X.Approximate inverse frequent itemset mining: privacy, complexity, and approximation. In:Proceedings–of the IEEE International Conference on Data Mining, ICDM;2005,482–489.
Chen, X,Orlowska, M.A further study on inverse frequent set mining.Lect Notes Comput Sci, 3584 LNAI2005,753–760.
Guo, Y.Reconstruction‐based association rule hiding. In:Proceedings of SIGMOD2007 Ph.D. Workshop on Innovative Database Research 2007 (IDAR2007);2007,51–56.
Zhu, Z,Du, W.K‐anonymous association rule hiding. In:Proceedings of the 5th International Symposium on Information, Computer and Communications Security, ASIACCS;2010,305–309.
Bertino, E,Fovino, IN,Provenza, LP.A framework for evaluating privacy preserving data mining algorithmsData Mining Knowledge Discov2005,11:121–154.
Verykios, VS,Bertino, E,Fovino, IN,Provenza, LP,Saygin, Y,Theodoridis, Y.State‐of‐the‐art in privacy preserving data mining.SIGMOD Record2004,33:50–57.
Bertino, E,Fovino, IN.Information driven evaluation of data hiding algorithms.Lect Notes Comput Sci2005,3589:418–427.
Bertino, E,Lin, D,Jiang, W.A survey of quantification of privacy preserving data mining algorithms. In:Aggarwal, CC,Yu, PS, eds.Privacy‐Preserving Data Mining—Models and Algorithms. Advances in Database Systems. Springer, Berlin;2008,34:183–205.
Gkoulalas‐Divanis, A,Verykios, VS.Association Rule Hiding for Data Mining. Kluwer;2010,3–144.
Aggarwal, C,Yu, PS, eds.Privacy‐Preserving Data Mining: Models and Algorithms. Springer, Berlin;2008.

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