Parsimonious downgrading and decision trees applied to the inference problem
Proceedings of the 1998 workshop on New security paradigms
Using unknowns to prevent discovery of association rules
ACM SIGMOD Record
Hiding Association Rules by Using Confidence and Support
IHW '01 Proceedings of the 4th International Workshop on Information Hiding
Privacy preserving mining of association rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Disclosure Limitation of Sensitive Rules
KDEX '99 Proceedings of the 1999 Workshop on Knowledge and Data Engineering Exchange
Privacy preserving frequent itemset mining
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
Security procedures for classification mining algorithms
Das'01 Proceedings of the fifteenth annual working conference on Database and application security
Privacy Preserving Association Rule Mining
RIDE '02 Proceedings of the 12th International Workshop on Research Issues in Data Engineering: Engineering E-Commerce/E-Business Systems (RIDE'02)
IEEE Transactions on Knowledge and Data Engineering
Association rule hiding in risk management for retail supply chain collaboration
Computers in Industry
Effective sanitization approaches to hide sensitive utility and frequent itemsets
Intelligent Data Analysis
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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.