Training Data Privacy

Training data privacy in machine learning focuses on developing methods to train models effectively without compromising the confidentiality of the data used. Current research emphasizes techniques like federated learning, differential privacy, and homomorphic encryption, often combined with generative models such as diffusion models, to achieve this goal. These approaches aim to balance the need for high-performing models with robust privacy protections against various attacks, including membership inference and data extraction. The impact of this research is significant, enabling the development and deployment of AI systems in sensitive domains like healthcare and finance while mitigating privacy risks.