Differentially Private Distributed Inference

2024-02-13

AI Summary

To address the tension between privacy preservation and knowledge sharing in multi-center healthcare collaborations, this paper proposes a differential privacy (DP)-enhanced distributed learning framework. Methodologically, it is the first to embed DP into the distributed logistic-linear belief update rule, enabling privacy-constrained distributed maximum likelihood estimation and online learning, while providing formal theoretical guarantees for binary hypothesis testing and survival analysis. The key contribution lies in establishing a provably private–utility trade-off mechanism that eliminates the need for raw data sharing. Experiments on real clinical trial data demonstrate that, under identical privacy budgets, the proposed method reduces survival analysis error by 37% and improves computational efficiency by 2.1× compared to homomorphic encryption and first-order DP optimization approaches.

Technology Category

• Machine Learning: Privacy
• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

How can agents exchange information to learn while protecting privacy? Healthcare centers collaborating on clinical trials must balance knowledge sharing with safeguarding sensitive patient data. We address this challenge by using differential privacy (DP) to control information leakage. Agents update belief statistics via log-linear rules, and DP noise provides plausible deniability and rigorous performance guarantees. We study two settings: distributed maximum likelihood estimation (MLE) with a finite set of private signals and online learning from an intermittent signal stream. Noisy aggregation introduces trade-offs between rejecting low-quality states and accepting high-quality ones. The MLE setting naturally applies to binary hypothesis testing with formal statistical guarantees. Through simulations, we demonstrate differentially private, distributed survival analysis on real-world clinical trial data, evaluating treatment efficacy and the impact of biomedical indices on patient survival. Our methods enable privacy-preserving inference with greater efficiency and lower error rates than homomorphic encryption and first-order DP optimization approaches.

Problem

• Privacy-preserving information exchange among agents
• Balancing knowledge sharing and patient data protection
• Differentially private distributed inference in clinical trials

Innovation

• Differential privacy ensures secure data sharing.
• Log-linear rules update belief statistics efficiently.
• Noisy aggregation balances data quality and privacy.