2 papers
math.ST2026
Differential privacy statistical inference for a directed graph network model with covariates
Jing Luo, Hong Qin, Zhimeng Xu
Network data typically contain sensitive relational information, where direct release or sharing may lead to non-negligible privacy violations without proper statistical safeguards…
cs.CR2026
PAC-DP: Personalized Adaptive Clipping for Differentially Private Federated Learning
Hao Zhou, Siqi Cai, Hua Dai +3
Differential privacy (DP) is crucial for safeguarding sensitive client information in federated learning (FL), yet traditional DP-FL methods rely predominantly on fixed gradient cl…