Privacy-Preserving Machine Learning: Techniques and Challenges

Privacy-preserving machine learning (PPML) aims to develop methods that enable model training and inference without compromising the privacy of the underlying data. This is particularly important in domains like healthcare, where patient data is highly sensitive.\n\nThis article explores techniques such as federated learning, differential privacy, secure multi-party computation, and homomorphic encryption. We discuss how these approaches enable collaborative model development while keeping raw data secure.\n\nWe also address the challenges in implementing these techniques, including computational overhead, accuracy trade-offs, and the need for standardized evaluation metrics.