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October 20, 2025Open Access

A Survey On Secure Machine Learning

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Authors

TLTaobo LiaoTLTaoran LiPNP. Nadkarni

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Overview

This review uncovers the synergy between secure multiparty computation and machine learning practices, highlighting privacy improvements.

Key Points

  • Recent advances in secure multiparty computation showcase enhanced privacy in collaborative learning workflows.
  • MPC's integration with machine learning allows for training on encrypted datasets while maintaining data confidentiality.
  • Innovative software frameworks streamline the implementation of MPC for machine learning, improving overall performance.
  • The survey emphasizes the necessity of cryptographic protocols and federated learning to protect against adversarial threats.

Cite This Study

Liao et al. (2025) studied this question.

synapsesocial.com/papers/68f5c338e2d8b128426459c5https://doi.org/10.48550/arxiv.2505.15124
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  1. 1Secure Multiparty Computation for Privacy‐Preserving Machine Learning in Healthcare: A Comprehensive Survey2025 · 5 citations
  2. 2State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey2024 · 7 citations
  3. 3Comprehensive Benchmarking of Secure Computation Technologies for Machine Learning on General Purpose Hardware2026
  4. 4Enabling Privacy-Preserving Machine Learning with Secure Multi-Party Computation2025
  5. 5State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey2024