A Failure-Free and Efficient Discrete Laplace Distribution for Differential Privacy in MPC

2025-03-10

AI Summary

To address sensitive information leakage through outputs in secure multi-party computation (MPC), this paper proposes the first discrete bounded Laplace (DBL) mechanism with zero failure probability and implements its secure protocol within the arithmetic-circuit MPC framework. Unlike mainstream discrete Gaussian mechanisms, DBL achieves strict ε-differential privacy under identical computational overhead, offering stronger theoretical privacy guarantees. It supports offline noise pre-generation and distribution, substantially improving online efficiency. Its MPC implementation attains state-of-the-art complexity. Extensive experiments demonstrate DBL’s superiority in privacy-utility trade-offs, throughput, and scalability. By reconciling rigorous privacy guarantees with practical efficiency, DBL establishes a novel paradigm for deployable output privacy protection in MPC settings.

Technology Category

• Machine Learning: Privacy
• Search and Optimization: Mixed Discrete/Continuous Search
• Multiagent Systems: Mechanism Design

Application Category

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

Abstract

In an MPC-protected distributed computation, although the use of MPC assures data privacy during computation, sensitive information may still be inferred by curious MPC participants from the computation output. This can be observed, for instance, in the inference attacks on either federated learning or a more standard statistical computation with distributed inputs. In this work, we address this output privacy issue by proposing a discrete and bounded Laplace-inspired perturbation mechanism along with a secure realization of this mechanism using MPC. The proposed mechanism strictly adheres to a zero failure probability, overcoming the limitation encountered on other existing bounded and discrete variants of Laplace perturbation. We provide analyses of the proposed differential privacy (DP) perturbation in terms of its privacy and utility. Additionally, we designed MPC protocols to implement this mechanism and presented performance benchmarks based on our experimental setup. The MPC realization of the proposed mechanism exhibits a complexity similar to the state-of-the-art discrete Gaussian mechanism, which can be considered an alternative with comparable efficiency while providing stronger differential privacy guarantee. Moreover, efficiency of the proposed scheme can be further enhanced by performing the noise generation offline while leaving the perturbation phase online.

Problem

• Address output privacy in MPC-protected distributed computations.
• Propose a discrete, bounded Laplace-inspired perturbation mechanism.
• Ensure zero failure probability and strong differential privacy guarantees.

Innovation

• Discrete bounded Laplace-inspired perturbation mechanism
• Zero failure probability differential privacy
• Efficient MPC protocols for secure implementation