Local Differential Privacy

Local differential privacy (LDP) enhances data privacy by adding noise to individual data points before aggregation, preventing the reconstruction of original data even with access to the aggregate. Current research focuses on improving the accuracy of machine learning models trained with LDP data, exploring techniques like randomized response mechanisms, Bayesian methods, and adaptive noise addition, often within federated learning frameworks and applied to various data types including graphs and time series. This field is significant because it allows for collaborative data analysis and machine learning while providing strong privacy guarantees, impacting diverse applications from healthcare and finance to recommender systems and smart grids.