Secure Multiparty Computation for Privacy‐Preserving Machine Learning in Healthcare: A Comprehensive Survey

ABSTRACT Privacy‐preserving machine learning (PPML) using secure multiparty computation (SMC) is emerging as a promising approach for enabling collaborative data analysis in healthcare while protecting sensitive patient information. This survey provides a comprehensive overview of SMC‐based PPML met...

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Published in:Wiley interdisciplinary reviews. Computational statistics Vol. 17; no. 3; pp. e70046 - n/a
Main Authors: Naresh, Vankamamidi S., Venkata Raju, A., Srinivasa Rao, O.
Format: Journal Article
Language:English
Published: Hoboken, USA John Wiley & Sons, Inc 01.09.2025
Wiley Subscription Services, Inc
Subjects:
Collaboration
Computation
Computer applications
Cryptography
Data analysis
Federated learning
Genomes
Genomic analysis
Health care
Inference
Informatics
Learning algorithms
Machine learning
Polls & surveys
Privacy
privacy preservation
Regulatory compliance
secure multiparty computation
Statistical analysis
Surveys
ISSN:1939-5108, 1939-0068
Online Access:Get full text
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Summary:ABSTRACT Privacy‐preserving machine learning (PPML) using secure multiparty computation (SMC) is emerging as a promising approach for enabling collaborative data analysis in healthcare while protecting sensitive patient information. This survey provides a comprehensive overview of SMC‐based PPML methods, their applications in healthcare, and the associated challenges. We first introduce the fundamental concepts of SMC and compare it with other privacy‐preserving techniques such as homomorphic encryption (HE) and differential privacy. We then discuss various privacy attacks on machine learning (ML) models, including model extraction, membership inference, and model inversion attacks. The survey examined the key applications of SMC in healthcare machine learning, such as collaborative model training, secure federated learning, privacy‐preserving inference, and secure genome analysis. We highlight the advantages of using SMC for PPML in healthcare, including enhanced data privacy, regulatory compliance, and preservation of data utility. The study also analyzed the major challenges in implementing SMC‐based PPML, including computational overhead, scalability issues, and implementation complexity. Finally, we discuss future research directions, including improving scalability, developing hybrid privacy‐preserving techniques, and addressing regulatory considerations. This survey aimed to provide researchers and practitioners with a comprehensive understanding of the current state and future prospects of SMC‐based PPML in healthcare. This article is categorized under: Algorithms and Computational Methods > Networks and Security Applications of Computational Statistics > Health and Medical Data/Informatics This graphical illustrates the role of Secure Multiparty Computation (SMC) in Privacy‐Preserving Machine Learning (PPML) within healthcare. It highlights techniques such as homomorphic encryption, differential privacy, and federated learning to secure sensitive medical data while mitigating adversarial threats like inversion attacks and membership inference.
Bibliography:The authors received no specific funding for this work.
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ISSN:1939-5108
1939-0068
DOI:10.1002/wics.70046