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Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
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Use HypE to Hide Association Rules by Adding Items.

Peng Cheng1, Chun-Wei Lin2, Jeng-Shyang Pan2

  • 1College of Computer and Information Science, Southwest University, Chongqing, P.R. China; Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, Guangdong, P.R. China.

Plos One
|June 13, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a novel evolutionary multi-objective optimization (EMO) method for association rule hiding. The approach effectively conceals sensitive data relationships by minimizing side effects, preserving valuable non-sensitive knowledge.

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Area of Science:

  • Data mining and knowledge discovery
  • Database security and privacy preservation
  • Evolutionary computation and optimization

Background:

  • Business collaboration often involves data sharing for insights.
  • Sensitive data relationships pose challenges for secure data sharing.
  • Existing methods for association rule hiding may damage non-sensitive knowledge.

Purpose of the Study:

  • To develop an effective association rule hiding method.
  • To minimize the negative impact on non-sensitive data relationships during hiding.
  • To address the challenge of concealing sensitive association rules.

Main Methods:

  • Proposed a hiding method based on evolutionary multi-objective optimization (EMO).
  • Utilized item insertion into the database to reduce sensitive rule confidence.
  • Employed the HypE algorithm to identify transactions for modification.
  • Minimized side effects as optimization goals.

Main Results:

  • The proposed EMO-based method effectively hides sensitive association rules.
  • Demonstrated fewer damages to non-sensitive knowledge compared to other methods in most cases.
  • Validated through experiments on real-world datasets.

Conclusions:

  • The EMO approach offers an effective solution for association rule hiding.
  • The method balances the need for privacy with the preservation of data utility.
  • This technique enhances secure data sharing in collaborative environments.