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Utility-Privacy Trade-Off in Distributed Machine Learning Systems.

Xia Zeng1,2, Chuanchuan Yang2,3, Bin Dai1,2

  • 1School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China.

Entropy (Basel, Switzerland)
|September 23, 2022
PubMed
Summary
This summary is machine-generated.

Distributed machine learning (DML) faces privacy risks from local gradient analysis. This study uses differential privacy (DP) to analyze the utility-privacy trade-off in DML, offering insights into optimal noise for enhanced security.

Keywords:
Gaussian noisedifferential privacydistributed machine learningmutual informationtrade-off

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Area of Science:

  • Distributed Machine Learning
  • Information Theory
  • Cybersecurity

Background:

  • Clients' sensitive data can be inferred from local gradient parameters in Distributed Machine Learning (DML), despite data not being directly shared.
  • The analysis of local gradients poses a significant privacy risk to individual clients participating in DML model training.

Purpose of the Study:

  • To investigate the utility-privacy trade-off in DML when employing differential privacy (DP) mechanisms.
  • To analyze different scenarios of client data dependency and noise distribution in DP-enhanced DML.

Main Methods:

  • Utilized information-theoretic concepts, specifically mutual information and conditional mutual information, to quantify utility and privacy.
  • Examined three distinct cases: independent client parameters with independent DP noise, and dependent client parameters with independent/dependent DP noise.
  • Derived the optimal noise variance for maximizing utility under specific privacy constraints.

Main Results:

  • Established the relationship between utility and privacy for the considered DML scenarios with DP.
  • Identified optimal noise variances that balance model utility with privacy protection levels.
  • Validated theoretical findings through numerical simulations.

Conclusions:

  • Differential privacy effectively mitigates privacy risks in DML by protecting local parameters.
  • The study provides a theoretical framework for understanding and optimizing the utility-privacy balance in DP-enhanced DML systems.
  • Findings offer practical guidance for implementing secure DML solutions.