Work

Differentially Private Online Community Detection for Censored Block Models: Algorithms and Fundamental Limits

Year: 2025

Type: article

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.

Source: IEEE Transactions on Information Forensics and Security

DOI: 10.1109/tifs.2025.3592556

Authors: Mohamed Seif, Liyan Xie, Andrea J. Goldsmith, H. Vincent Poor

Institutions: Princeton University, University of Minnesota

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Air Force Office of Scientific Research

Awards: G2745202827, Collaborative Research: SaTC: CORE: Medium: Foundations of Trust-Centered Multi-Agent Distributed Coordination