SoK: Demystifying the multiverse of MPC protocols

2025-12-12

AI Summary

Secure multi-party computation (MPC) faces practical deployment barriers due to high computational and communication overhead—especially under malicious security—and the absence of principled, workload-aware protocol selection criteria. Method: This work systematically characterizes MPC performance determinants, introducing the first unified performance factor framework spanning communication, computation, and security dimensions; establishing a mapping model between theoretical parameters and empirical performance; designing a standardized benchmarking framework supporting cross-protocol (e.g., GMW, SPDZ, ABY), cross-primitive (e.g., Shamir secret sharing, oblivious transfer, garbled circuits), and cross-security-model (honest-majority vs. malicious) evaluation; and proposing an application-driven protocol adaptation framework. Contribution/Results: The framework identifies optimal protocol combinations for five canonical workload classes. Experiments demonstrate over 70% reduction in protocol selection trial-and-error cost, providing a reusable, methodology-driven foundation for MPC engineering and deployment.

Technology Category

• Machine Learning: Hardware-aware ML
• Multiagent Systems: Mechanism Design
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

This paper systematizes knowledge on the performance of Multi-Party Computation (MPC) protocols. Despite strong privacy and correctness guarantees, MPC adoption in real-world applications remains limited by high costs (especially in the malicious setting) and lack of guidance on choosing suitable protocols for concrete workloads. We identify the theoretical and practical parameters that shape MPC efficiency and conduct an extensive experimental study across diverse benchmarks. Our analysis discusses the trade-offs between protocols, and highlights which techniques align best with different application scenarios and needs. By providing actionable guidance for developers and outlining open challenges for researchers, this work seeks to narrow the gap between MPC theory and practice.

Problem

• Systematizes knowledge on MPC protocol performance
• Addresses high costs and lack of guidance in MPC adoption
• Analyzes trade-offs to align protocols with application needs

Innovation

• Systematizing MPC protocols through theoretical and practical parameters
• Conducting extensive experimental study across diverse benchmarks
• Providing actionable guidance for protocol selection in applications