A Comparative Assessment of Multi-Party Computation Protocols: Performance Metrics and Security Implications

Abstract

 Multi-Party Computation (MPC) protocols enable secure computations across multiple parties while preserving the privacy of their inputs. This paper provides a comparative assessment of various MPC protocols by evaluating their performance metrics and security implications. The study focuses on established protocols such as Yao's Garbled Circuits, Secure Multi-Party Computation (SMPC) with Homomorphic Encryption, and Secret Sharing Schemes. We analyze their efficiency in terms of computational and communication overhead, as well as their robustness against different types of attacks. The findings aim to guide the selection of appropriate MPC protocols for various applications based on specific security requirements and performance constraints.