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
Data mining: concepts and techniques
Data mining: concepts and techniques
Privacy Preserving Data Mining
CRYPTO '00 Proceedings of the 20th Annual International Cryptology Conference on Advances in Cryptology
The inference problem: a survey
ACM SIGKDD Explorations Newsletter
Disclosure Limitation of Sensitive Rules
KDEX '99 Proceedings of the 1999 Workshop on Knowledge and Data Engineering Exchange
Two new techniques for hiding sensitive itemsets and their empirical evaluation
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
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In this paper, we propose a new heuristic algorithm called the QIBC algorithm that improves the privacy of sensitive knowledge (as itemsets) by blocking more inference channels. We show that the existing sanitizing algorithms for such task have fundamental drawbacks. We show that previous methods remove more knowledge than necessary for unjustified reasons or heuristically attempt to remove the minimum frequent non-sensitive knowledge but leave open inference channels that lead to discovery of hidden sensitive knowledge. We formalize the refined problem and prove it is NP-hard. Finally, experimental results show the practicality of the new QIBC algorithm.