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 association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Pushing Support Constraints Into Association Rules Mining
IEEE Transactions on Knowledge and Data Engineering
Disclosure Limitation of Sensitive Rules
KDEX '99 Proceedings of the 1999 Workshop on Knowledge and Data Engineering Exchange
IEEE Transactions on Knowledge and Data Engineering
A Novel Method for Protecting Sensitive Knowledge in Association Rules Mining
COMPSAC '05 Proceedings of the 29th Annual International Computer Software and Applications Conference - Volume 01
A Border-Based Approach for Hiding Sensitive Frequent Itemsets
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Template-Based Privacy Preservation in Classification Problems
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
A unified framework for protecting sensitive association rules in business collaboration
International Journal of Business Intelligence and Data Mining
Mining association rules with multiple minimum supports using maximum constraints
International Journal of Approximate Reasoning
Ask a better question, get a better answer a new approach to private data analysis
ICDT'07 Proceedings of the 11th international conference on Database Theory
| Hi-index | 0.00 |
Frequent pattern mining is a popular topic in data mining. With the advance of this technique, privacy issues attract more and more attention in recent years. In this field, previous works based hiding sensitive information on a uniform support threshold or a disclosure threshold. However, in practical applications, we probably need to apply different support thresholds to different itemsets for reflecting their significance. In this paper, we propose a new hiding strategy to protect sensitive frequent patterns with multiple sensitive thresholds. Based on different sensitive thresholds, the sanitized dataset is able to highly fulfill user requirements in real applications, while preserving more information of the original dataset. Empirical studies show that our approach can protect sensitive knowledge well not only under multiple thresholds, but also under a uniform threshold. Moreover, the quality of the sanitized dataset can be maintained.