Privacy-Preserving AI: Secure Multi-Party Computation for Collaborative Machine Learning
Abstract
Collaborative machine learning enables multiple organizations to train models on combined datasets without compromising data privacy. This paper explores the use of secure multi-party computation (SMPC) to ensure data confidentiality during collaborative training. The proposed framework integrates SMPC protocols with gradient descent algorithms, enabling secure and efficient model updates. Experiments on real-world datasets from finance and healthcare show that the framework maintains high model accuracy while preserving privacy, making it suitable for sensitive data-sharing scenarios.
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