On Privacy-Preserved Machine Learning Using Secure Multi-Party Computing: Techniques and Trends

The rapid adoption of machine learning in sensitive domains, such as healthcare, finance, and government services, has heightened the need for robust, privacy-preserving techniques. Traditional machine learning approaches lack built-in privacy mechanisms, exposing sensitive data to risks, which motivates the development of Privacy-Preserving Machine Learning (PPML) methods. Despite significant advances in PPML, a comprehensive and focused exploration of Secure Multi-Party Computing (SMPC) within this context remains underdeveloped. This review aims to bridge this knowledge gap by systematically analyzing the role of SMPC in PPML, offering a structured overview of current techniques, challenges, and future directions. Using a semi-systematic mapping study methodology, this paper surveys recent literature spanning SMPC protocols, PPML frameworks, implementation approaches, threat models, and performance metrics. Emphasis is placed on identifying trends, technical limitations, and comparative strengths of leading SMPC-based methods. Our findings reveal that while SMPC offers strong cryptographic guarantees for privacy, challenges such as computational overhead, communication costs, and scalability persist. The paper also discusses critical vulnerabilities, practical deployment issues, and variations in protocol efficiency across use cases.