Privacy Advantage

Privacy advantage in machine learning explores methods to enhance data protection during model training and deployment, primarily focusing on mitigating the risk of inferring sensitive information from shared data or model parameters. Current research investigates this across various architectures, including federated learning (both centralized and decentralized), variational quantum circuits, and differentially private algorithms employing random projections or matrix encryption. These efforts aim to quantify and minimize privacy leakage, balancing data utility with robust privacy guarantees, impacting the development of secure and trustworthy AI systems across diverse applications.