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Medical privacy protection based on granular computing.

Da-Wei Wang1, Churn-Jung Liau, Tsan-Sheng Hsu

  • 1Institute of Information Science, Academia Sinica, Nankang, 11529 Taipei, Taiwan. wdw@iis.sinica.edu.tw

Artificial Intelligence in Medicine
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Summary
This summary is machine-generated.

This study introduces two new quantitative measures for privacy invasion: total cost and average benefit. These criteria, based on granular computing, offer a more robust approach to privacy measurement in data release.

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Area of Science:

  • Computer Science
  • Information Security
  • Data Privacy

Background:

  • Existing privacy formulations often lack quantitative depth.
  • Deterministic privacy models have limitations in capturing nuanced privacy invasion.
  • Granular computing offers a unified framework for data analysis.

Purpose of the Study:

  • To propose novel quantitative criteria for measuring privacy invasion.
  • To address the inadequacy of deterministic privacy formulations.
  • To provide a unified framework for privacy measurement using granular computing.

Main Methods:

  • Developed two quantitative criteria: total cost and average benefit.
  • Utilized granular computing methodology for a unified measurement framework.
  • Implemented the criteria in a prototype system, Cellsecu 2.0.

Main Results:

  • The proposed criteria quantitatively measure privacy invasion.
  • Total cost measures the effort to uncover private information.
  • Average benefit quantifies the recipient's gain from released data.

Conclusions:

  • The new criteria offer a more comprehensive approach to privacy measurement.
  • Granular computing provides a unified methodology for these quantitative measures.
  • The Cellsecu 2.0 system demonstrates the practical implementation of these privacy criteria.