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Cross-silo Federated Learning with Record-level Personalized Differential Privacy.

Junxu Liu1, Jian Lou2, Li Xiong3

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Summary
This summary is machine-generated.

This study introduces rPDP-FL, a novel federated learning framework using personalized differential privacy at the record level. It enhances data protection by accommodating varying privacy needs, outperforming existing methods.

Keywords:
Differential PrivacyFederated LearningPersonalized Privacy Protection

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Area of Science:

  • Computer Science
  • Machine Learning
  • Cybersecurity

Background:

  • Federated learning (FL) uses differential privacy to protect client data.
  • Current FL privacy methods offer uniform protection, which may not suit individual data record needs.
  • Personalized differential privacy in cross-silo FL remains an underexplored area.

Purpose of the Study:

  • To introduce a novel framework for record-level personalized differential privacy in cross-silo federated learning.
  • To address the challenge of determining optimal per-record sampling probabilities for personalized privacy budgets.
  • To improve privacy preservation and performance in FL systems.

Main Methods:

  • Developed the rPDP-FL framework with a two-stage hybrid sampling scheme (client-level and record-level).
  • Introduced the Simulation-CurveFitting method to model the nonlinear relationship between sampling probability and privacy budget.
  • Derived a mathematical model for per-record sampling probability (q) based on personalized privacy budget (ε).

Main Results:

  • The proposed rPDP-FL framework effectively accommodates varying privacy requirements at the record level.
  • Simulation-CurveFitting successfully identified the correlation between sampling probability and privacy budget.
  • The derived mathematical model enables precise control over personalized privacy.
  • Evaluations show significant performance gains compared to non-personalized privacy baselines.

Conclusions:

  • Record-level personalized differential privacy is crucial for advanced federated learning applications.
  • The rPDP-FL framework and Simulation-CurveFitting method offer a robust solution for personalized privacy in FL.
  • This approach enhances data security and model performance by respecting individual data privacy needs.