Privacy Parameter

Privacy parameters, crucial in differential privacy (DP) mechanisms, control the trade-off between data privacy and model utility in machine learning. Current research focuses on improving the accuracy of DP models while mitigating the risks of membership inference attacks, particularly by exploring optimal parameter selection for various algorithms like federated learning and collaborative clustering, and by developing techniques to account for heterogeneous privacy requirements across datasets. This work is significant because it aims to enable the responsible use of sensitive data in machine learning applications, bridging the gap between theoretical privacy guarantees and practical implementation challenges.