 Local differential privacy

Local Differential Privacy (LDP) is a privacy-preserving framework that allows for the collection and analysis of user data while ensuring that individual data points remain private. It is a variant of differential privacy, which is a technique designed to provide mathematical guarantees that the output of a data analysis will not reveal too much information about any individual in the dataset. In traditional differential privacy, a central authority collects and aggregates data from individuals and then adds noise to the aggregated data to obscure individual contributions.