Unknown

Dataset Information

0

Use HypE to Hide Association Rules by Adding Items.

ABSTRACT: During business collaboration, partners may benefit through sharing data. People may use data mining tools to discover useful relationships from shared data. However, some relationships are sensitive to the data owners and they hope to conceal them before sharing. In this paper, we address this problem in forms of association rule hiding. A hiding method based on evolutionary multi-objective optimization (EMO) is proposed, which performs the hiding task by selectively inserting items into the database to decrease the confidence of sensitive rules below specified thresholds. The side effects generated during the hiding process are taken as optimization goals to be minimized. HypE, a recently proposed EMO algorithm, is utilized to identify promising transactions for modification to minimize side effects. Results on real datasets demonstrate that the proposed method can effectively perform sanitization with fewer damages to the non-sensitive knowledge in most cases.

SUBMITTER: Cheng P 

PROVIDER: S-EPMC4466550 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

Json Xml
altmetric image

Publications

Use HypE to Hide Association Rules by Adding Items.

Cheng Peng P   Lin Chun-Wei CW   Pan Jeng-Shyang JS  

PloS one 20150612 6

During business collaboration, partners may benefit through sharing data. People may use data mining tools to discover useful relationships from shared data. However, some relationships are sensitive to the data owners and they hope to conceal them before sharing. In this paper, we address this problem in forms of association rule hiding. A hiding method based on evolutionary multi-objective optimization (EMO) is proposed, which performs the hiding task by selectively inserting items into the da  ...[more]