TY - JOUR
T1 - Hide Association Rules with Fewer Side Effects
AU - Cheng, Peng
AU - Lee, Ivan
AU - Pan, Jeng-Shyang
AU - Lin, Chun-Wei
AU - Roddick, John
PY - 2015/10/1
Y1 - 2015/10/1
N2 - 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.
AB - 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.
KW - Association rule hiding
KW - Border rules
KW - Side effects
UR - http://doi.org/10.1587/transinf.2014EDP7345
UR - http://www.scopus.com/inward/record.url?scp=84942942270&partnerID=8YFLogxK
U2 - 10.1587/transinf.2014EDP7345
DO - 10.1587/transinf.2014EDP7345
M3 - Article
VL - E98-D
SP - 1788
EP - 1798
JO - IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
JF - IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
SN - 0916-8532
IS - 10
ER -