Private Neural

Private neural networks aim to train machine learning models, particularly neural networks, while preserving the privacy of the training data, often using differential privacy techniques. Current research focuses on adapting various neural network architectures, including spiking neural networks and generative models for graphs, to incorporate differential privacy mechanisms like differentially private stochastic gradient descent and noise addition, often with modifications to improve accuracy while maintaining privacy guarantees. This field is crucial for enabling the use of sensitive data in machine learning applications, such as healthcare and contact tracing, where privacy is paramount, and its advancements are driving the development of more privacy-preserving AI systems.