Privacy Constraint

Privacy constraints in machine learning focus on developing methods to train and deploy models while safeguarding sensitive data, primarily through techniques like differential privacy. Current research emphasizes improving the accuracy of differentially private models by employing adaptive noise allocation, exploring alternative neural network architectures like Kolmogorov-Arnold networks, and leveraging federated learning to decentralize training and enhance privacy. This field is crucial for responsible AI development, enabling the use of sensitive data in various applications while mitigating privacy risks and ensuring compliance with regulations.