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Area of Science:

  • Medical Informatics
  • Data Science
  • Privacy-Preserving Technologies

Background:

  • Big data in medicine necessitates secure data release methods.
  • Traditional k-anonymity models are vulnerable to sophisticated privacy attacks.
  • Ensuring patient privacy is paramount for advancing medical research.

Purpose of the Study:

  • To develop a novel data publishing method for medical big data.
  • To enhance patient privacy protection beyond existing models.
  • To maintain data validity and availability after release.

Main Methods:

  • Attribute correlation calculation to preserve data validity.
  • Integration of differential privacy and tree models.
  • Proposal of the Attribute Association-based Differential Privacy classification tree (ACDP-Tree) method.

Main Results:

  • Simulation experiments on real medical datasets were conducted.
  • The ACDP-Tree method demonstrated effectiveness in protecting patient privacy.
  • Data validity and availability were maintained to a significant extent.

Conclusions:

  • The ACDP-Tree method offers improved privacy protection for medical data release.
  • This approach addresses limitations of traditional anonymity models.
  • It facilitates secure utilization of medical big data for research and development.