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.
Articles by others on the same topic
There are currently no matching articles.