Hide Association Rules with Fewer Side Effects

Peng Cheng, Ivan Lee, Jeng-Shyang Pan, Chun-Wei Lin, John Roddick

    Research output: Contribution to journalArticlepeer-review

    10 Citations (Scopus)


    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.

    Original languageEnglish
    Pages (from-to)1788-1798
    Number of pages11
    Issue number10
    Publication statusPublished - 1 Oct 2015


    • Association rule hiding
    • Border rules
    • Side effects


    Dive into the research topics of 'Hide Association Rules with Fewer Side Effects'. Together they form a unique fingerprint.

    Cite this