Enhancing Scalability of Metric Differential Privacy via Secret Dataset Partitioning and Benders Decomposition

arXiv:2405.04344v1 Announce Type: cross
Abstract: Metric Differential Privacy (mDP) extends the concept of Differential Privacy (DP) to serve as a new paradigm of data perturbation. It is designed to protect secret data represented in general metric space, such as text data encoded as word embeddings or geo-location data on the road network or grid maps. To derive an optimal data perturbation mechanism under mDP, a widely used method is linear programming (LP), which, however, might suffer from a polynomial explosion …

arxiv concept cs.ai cs.cr data dataset differential privacy general geo-location location metric paradigm privacy protect scalability secret space text word