Secure Multi-Party Computation Protocols for Privacy-Preserving Data Analysis

Abstract

Secure Multi-Party Computation (SMPC) has emerged as a critical cryptographic technique enabling multiple parties to collaboratively compute functions over their private inputs without revealing the underlying data. This paper explores various SMPC protocols designed for privacy-preserving data analysis, focusing on their efficiency, scalability, and security guarantees. We review recent advancements in both semi-honest and malicious security models, highlighting the trade-offs between computational complexity, communication overhead, and real-world applicability. Additionally, we discuss practical implementations of SMPC in fields such as healthcare, finance, and network security, emphasizing their role in enabling privacy-preserving machine learning and statistical analysis. Finally, we identify open challenges, including performance bottlenecks, scalability issues, and integration with existing data processing frameworks, paving the way for future research in privacy-preserving collaborative analytics.