Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Using unknowns to prevent discovery of association rules
ACM SIGMOD Record
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
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
IEEE Transactions on Knowledge and Data Engineering
A Framework for Evaluating Privacy Preserving Data Mining Algorithms*
Data Mining and Knowledge Discovery
A Border-Based Approach for Hiding Sensitive Frequent Itemsets
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
An integer programming approach for frequent itemset hiding
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Hiding Sensitive Association Rules with Limited Side Effects
IEEE Transactions on Knowledge and Data Engineering
Maximizing Accuracy of Shared Databases when Concealing Sensitive Patterns
Information Systems Research
A MaxMin approach for hiding frequent itemsets
Data & Knowledge Engineering
A unified framework for protecting sensitive association rules in business collaboration
International Journal of Business Intelligence and Data Mining
Using TF-IDF to hide sensitive itemsets
Applied Intelligence
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In the paper we point out that the released dataset of an association rule hiding method may have severe privacy problem since they all achieve to minimize the side effects on the original dataset. We show that an attacker can discover the hidden sensitive association rules with high confidence when there is not enough "blindage". We give a detailed analysis of the attack and propose a novel association rule hiding metric, K-anonymous. Based on the K-anonymous metric, we present a framework to hide a group of sensitive association rules while it is guaranteed that the hidden rules are mixed with at least other K-1 rules in the specific region. Several heuristic algorithms are proposed to achieve the hiding process. Experiment results are reported to show the effectiveness and efficiency of the proposed approaches.