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Hiding Decision Tree Rules in Medical Data: A Case Study.

Georgios Feretzakis1, Dimitris Kalles1, Vassilios S Verykios1

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

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Summary
This summary is machine-generated.

This study introduces a heuristic method to protect sensitive patterns in health data during decision tree analysis. This approach enhances data usability for third parties by hiding specific rules without compromising raw data access.

Keywords:
Decision treesdata sharinghiding decision tree rulesmedical dataprivacy preserving

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Area of Science:

  • Computer Science
  • Health Informatics
  • Data Security

Background:

  • Data sharing is increasing among health organizations.
  • Protecting sensitive patterns within shared data is crucial.
  • Existing methods like output disturbance and encryption can limit data usability.

Purpose of the Study:

  • To propose a heuristic approach for protecting sensitive patterns during decision tree induction.
  • To ensure raw data remains accessible for third-party analysis.
  • To offer a more usable alternative to output disturbance or encryption.

Main Methods:

  • A heuristic method is applied to hide arbitrary rules during binary decision tree derivation.
  • Focuses on protecting specific patterns within the data.
  • Evaluates the method against existing heuristic solutions.

Main Results:

  • The proposed method effectively hides sensitive patterns.
  • It maintains the usability of the raw data.
  • It is preferable to output disturbance or encryption methods.

Conclusions:

  • The heuristic rule-hiding approach is effective for sensitive pattern protection in decision tree analysis.
  • This method facilitates secure data sharing while preserving data utility.
  • It offers a practical solution for health organizations needing to share data responsibly.