Efficient algorithms for distortion and blocking techniques in association rule hiding |
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Authors: | Vassilios S Verykios Emmanuel D Pontikakis Yannis Theodoridis Liwu Chang |
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Affiliation: | (1) Department of Computer and Communication Engineering, University of Thessaly, Volos, Greece;(2) Department of Computer Science, Stanford University, Stanford, CA, USA;(3) Department of Informatics, University of Piraeus, Piraeus, Greece;(4) Naval Research Laboratory, Washington, DC, USA |
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Abstract: | Data mining provides the opportunity to extract useful information from large databases. Various techniques have been proposed
in this context in order to extract this information in the most efficient way. However, efficiency is not our only concern
in this study. The security and privacy issues over the extracted knowledge must be seriously considered as well. By taking
this into consideration, we study the procedure of hiding sensitive association rules in binary data sets by blocking some
data values and we present an algorithm for solving this problem. We also provide a fuzzification of the support and the confidence
of an association rule in order to accommodate for the existence of blocked/unknown values. In addition, we quantitatively
compare the proposed algorithm with other already published algorithms by running experiments on binary data sets, and we
also qualitatively compare the efficiency of the proposed algorithm in hiding association rules. We utilize the notion of
border rules, by putting weights in each rule, and we use effective data structures for the representation of the rules so
as (a) to minimize the side effects created by the hiding process and (b) to speed up the selection of the victim transactions.
Finally, we study the overall security of the modified database, using the C4.5 decision tree algorithm of the WEKA data mining
tool, and we discuss the advantages and the limitations of blocking. |
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Keywords: | Data mining Privacy Association rules |
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