Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Using association rules for product assortment decisions: a case study
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
Using unknowns to prevent discovery of association rules
ACM SIGMOD Record
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Hiding Association Rules by Using Confidence and Support
IHW '01 Proceedings of the 4th International Workshop on Information Hiding
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
Sanitization of databases for refined privacy trade-offs
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
Data reduction approach for sensitive associative classification rule hiding
ADC '08 Proceedings of the nineteenth conference on Australasian database - Volume 75
A Heuristic Data Reduction Approach for Associative Classification Rule Hiding
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Associative classification rules hiding for privacy preservation
International Journal of Intelligent Information and Database Systems
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Many privacy preserving data mining algorithms attempt to selectively hide what database owners consider as sensitive. Specifically, in the association-rules domain, many of these algorithms are based on item-restriction methods; that is, removing items from some transactions in order to hide sensitive frequent itemsets. The infancy of this area has not produced clear methods neither evaluated those few available. However, determining what is most effective in protecting sensitive itemsets while not hiding non-sensitive ones as a side effect remains a crucial research issue. This paper introduces two new techniques that deal with scenarios where many itemsets of different sizes are sensitive. We empirically evaluate our two sanitization techniques and compare their efficiency as well as which has the minimum effect on the non-sensitive frequent itemsets.