Correlated Privacy Mechanisms for Differentially Private Distributed Mean Estimation

arXiv:2407.03289v1 Announce Type: cross
Abstract: Differentially private distributed mean estimation (DP-DME) is a fundamental building block in privacy-preserving federated learning, where a central server estimates the mean of $d$-dimensional vectors held by $n$ users while ensuring $(\epsilon,\delta)$-DP. Local differential privacy (LDP) and distributed DP with secure aggregation (SecAgg) are the most common notions of DP used in DP-DME settings with an untrusted server. LDP provides strong resilience to dropouts, colluding users, and malicious server attacks, but suffers from …

aggregation arxiv block building central cs.cr cs.it cs.lg delta differential privacy distributed epsilon federated federated learning local math.it mechanisms privacy private server