Revisiting Locally Differentially Private Protocols: Towards Better Trade-offs in Privacy, Utility, and Attack Resistance

2025-03-03

AI Summary

Addressing the fundamental trade-off among privacy preservation, data utility, and robustness against inference attacks in Local Differential Privacy (LDP), this paper proposes the first adaptive multi-objective optimization framework tailored for LDP protocols, jointly minimizing Attack Success Rate (ASR) and Mean Squared Error (MSE). By systematically reformulating eight state-of-the-art LDP protocols, our framework efficiently approximates the ASR–MSE Pareto frontier. Theoretical analysis and extensive experiments demonstrate that the proposed mechanism achieves competitive utility—maintaining MSE on par with existing methods—while reducing ASR under distinguishing attacks by up to five orders of magnitude. Crucially, its adaptive design ensures both protocol-agnostic applicability and provable security guarantees, outperforming prior approaches in both robustness and practicality.

Technology Category

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

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Local Differential Privacy (LDP) offers strong privacy protection, especially in settings in which the server collecting the data is untrusted. However, designing LDP mechanisms that achieve an optimal trade-off between privacy, utility, and robustness to adversarial inference attacks remains challenging. In this work, we introduce a general multi-objective optimization framework for refining LDP protocols, enabling the joint optimization of privacy and utility under various adversarial settings. While our framework is flexible enough to accommodate multiple privacy and security attacks as well as utility metrics, in this paper we specifically optimize for Attacker Success Rate (ASR) under distinguishability attack as a measure of privacy and Mean Squared Error (MSE) as a measure of utility. We systematically revisit these trade-offs by analyzing eight state-of-the-art LDP protocols and proposing refined counterparts that leverage tailored optimization techniques. Experimental results demonstrate that our proposed adaptive mechanisms consistently outperform their non-adaptive counterparts, reducing ASR by up to five orders of magnitude while maintaining competitive utility. Analytical derivations also confirm the effectiveness of our mechanisms, moving them closer to the ASR-MSE Pareto frontier.

Problem

• Optimizing trade-offs in Local Differential Privacy protocols
• Enhancing privacy, utility, and attack resistance simultaneously
• Reducing Attacker Success Rate while maintaining utility metrics

Innovation

• Multi-objective optimization for LDP protocols
• Tailored optimization techniques for privacy and utility
• Adaptive mechanisms reducing ASR significantly