Paper Info
Title
Hide association rules with fewer side effects
Abstract
Association rule mining is a powerful data mining tool, and it can be used to discover unknown patterns from large volumes of data. However, people often have to face the risk of disclosing sensitive information when data is shared with different organizations. The association rule mining techniques may be improperly used to find sensitive patterns which the owner is unwilling to disclose. One of the great challenges in association rule mining is how to protect the confidentiality of sensitive patterns when data is released. Association rule hiding refers to sanitize a database so that certain sensitive association rules cannot be mined out in the released database. In this study, we proposed a new method which hides sensitive rules by removing some items in a database to reduce the support or confidence levels of sensitive rules below specified thresholds. Based on the information of positive border rules and negative border rules contained in transactions, the proposed method chooses suitable candidates for modification aimed at reducing the side effects and the data distortion degree. Comparative experiments on real datasets and synthetic datasets demonstrate that the proposed method can hide sensitive rules with much fewer side effects and database modifications. Copyright © 2015 The Institute of Electronics, Information and Communication Engineers.
Year
DOI
Venue
2015
10.1587/transinf.2014EDP7345
IEICE Transactions on Information and Systems
Keywords
Field
DocType
Association rule hiding, side effects, border rules
Pattern recognition,Computer science,Association rule learning,Artificial intelligence,Natural language processing,Association rule hiding
Journal
Volume
Issue
ISSN
E98D
10
1745-1361
Citations 
PageRank 
References 
2
0.36
16
Authors
5
Name
Order
Citations
PageRank
Chengy Peng120.36
Ivan Lee218422.53
Pan Jeng-Shyang32466269.74
Chun-Wei Lin41484154.11
John F. Roddick51908331.20