Causal Discovery Under Local Privacy

arXiv:2311.04037v3 Announce Type: replace-cross
Abstract: Differential privacy is a widely adopted framework designed to safeguard the sensitive information of data providers within a data set. It is based on the application of controlled noise at the interface between the server that stores and processes the data, and the data consumers. Local differential privacy is a variant that allows data providers to apply the privatization mechanism themselves on their data individually. Therefore it provides protection also in contexts in which the …

abstract application arxiv causal consumers cs.ai cs.cr cs.lg data data set differential differential privacy discovery framework information noise privacy processes server set stat.me stores type