Open Access

REVIEW

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

Oshan Mudannayake^1,#, Amila Indika^2,#, Upul Jayasinghe², Gyu Myoung Lee^3,*, Janaka Alawatugoda⁴

1 School of Computing, University of Colombo, Colombo, 00700, Sri Lanka
2 Department of Computer Engineering, University of Peradeniya, Peradeniya, 20400, Sri Lanka
3 School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, L3 3AF, UK
4 Research and Innovations Centers Division, Rabdan Academy, Abu Dhabi, 22401, United Arab Emirates

* Corresponding Author: Gyu Myoung Lee. Email:
# These authors contributed equally to this work

Computers, Materials & Continua 2025, 85(2), 2527-2578. https://doi.org/10.32604/cmc.2025.068875

Received 09 June 2025; Accepted 13 August 2025; Issue published 23 September 2025

Abstract

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.

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Keywords

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