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Using Minimum Local Distortion to Hide Decision Tree Rules.

Georgios Feretzakis1, Dimitris Kalles1, Vassilios S Verykios1

  • 1School of Science and Technology, Hellenic Open University, 263 35 Patras, Greece.

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|December 3, 2020
PubMed
Summary
This summary is machine-generated.

Organizations can now share data securely using a novel heuristic approach to decision tree induction. This method preserves sensitive patterns while keeping raw data usable for analysis.

Keywords:
data sharingdecision treesentropyhiding rulesinformation gainprivacy preserving

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Area of Science:

  • Computer Science
  • Data Privacy
  • Machine Learning

Background:

  • Data sharing is prevalent across industries like finance, e-commerce, and healthcare.
  • Organizations need to protect sensitive patterns within shared datasets.
  • Existing privacy methods can limit data usability.

Purpose of the Study:

  • To propose a heuristic approach for preserving sensitive patterns during decision tree induction.
  • To enable secure data sharing without compromising proprietary information.
  • To offer an alternative to methods like output perturbation and cryptography.

Main Methods:

  • Developed a heuristic approach to identify and hide specific rules in binary decision trees.
  • Focused on minimizing the impact on other derived rules.
  • Ensured raw data remains accessible for public use.

Main Results:

  • The proposed method effectively hides sensitive decision tree rules.
  • Privacy preservation is achieved with minimal impact on the overall decision tree structure.
  • The approach maintains the usability of the raw data.

Conclusions:

  • The heuristic approach offers a practical solution for privacy-preserving decision tree induction.
  • This method balances data utility with the need for sensitive pattern protection.
  • It provides a valuable tool for organizations engaged in secure data sharing.