Privacy Preservation

Privacy preservation in machine learning focuses on enabling collaborative model training and deployment without compromising sensitive data. Current research emphasizes techniques like federated learning, differential privacy, and generative models (e.g., GANs) to achieve this, often employing architectures such as deep neural networks and large language models. These methods aim to balance the utility of trained models with robust privacy guarantees against various attacks, impacting fields ranging from healthcare and finance to social media and IoT applications. The ongoing challenge lies in finding optimal trade-offs between privacy protection, model accuracy, and computational efficiency.