Differentially Private Online Community Detection for Censored Block Models: Algorithms and Fundamental Limits
Social and Information Networks
2025-10-08 v2 Cryptography and Security
Information Theory
math.IT
Abstract
We study the private online change detection problem for dynamic communities, using a censored block model (CBM). We consider edge differential privacy (DP) in both local and central settings, and propose joint change detection and community estimation procedures for both scenarios. We seek to understand the fundamental tradeoffs between the privacy budget, detection delay, and exact community recovery of community labels. Further, we provide theoretical guarantees for the effectiveness of our proposed method by showing necessary and sufficient conditions for change detection and exact recovery under edge DP. Simulation and real data examples are provided to validate the proposed methods.
Keywords
Cite
@article{arxiv.2405.05724,
title = {Differentially Private Online Community Detection for Censored Block Models: Algorithms and Fundamental Limits},
author = {Mohamed Seif and Liyan Xie and Andrea J. Goldsmith and H. Vincent Poor},
journal= {arXiv preprint arXiv:2405.05724},
year = {2025}
}