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Related Experiment Video

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Selecting Optimal Subset to release under Differentially Private M-estimators from Hybrid Datasets.

Meng Wang1, Zhanglong Ji2, Hyeon-Eui Kim2

  • 1Department of Biomedical Informatics, University of California at San Diego, CA, 92093 U.S., and now is with the Department of Genetics, Stanford University, CA, 94305, U.S.

IEEE Transactions on Knowledge and Data Engineering
|July 24, 2018
PubMed
Summary

This study introduces a method for selecting optimal public data subsets for privacy-preserving research using differential privacy (DP). It guides effective use of public health data while protecting individual privacy.

Keywords:
M-estimatorsdifferential privacyhybrid datasets

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Area of Science:

  • Data privacy
  • Statistical learning
  • Health informatics

Background:

  • Growing concerns regarding health data sharing and privacy.
  • Need for methods to leverage public data for research without compromising private datasets.

Purpose of the Study:

  • To develop a method for selecting optimal public data subsets for M-estimators within differential privacy (DP).
  • To address the challenge of utilizing public information to enhance understanding of private datasets securely.

Main Methods:

  • Constructing weighted private density estimation from hybrid datasets under DP.
  • Analyzing the accuracy of DP M-estimators using hybrid datasets.
  • Developing an algorithm for optimal public data subset selection under DP.

Main Results:

  • The bias-variance tradeoff in M-estimator performance is characterized by the sample size of the released dataset.
  • An algorithm was developed to identify the optimal public data subset for release under DP.

Conclusions:

  • The findings provide a guideline for real-world applications of privacy-preserving data analysis.
  • Simulation studies and real-data applications validate the proposed method for optimal subset selection.