Private Online Community Detection for Censored Block Models

2024-05-09

arXiv.org

AI Summary

This work addresses privacy-preserving online community change detection in dynamic social networks under censorship. We propose a streaming statistical testing and private label recovery framework based on the Censored Block Model (CBM), enabling real-time detection and exact member label reconstruction under edge differential privacy (DP). Our contributions are threefold: (i) we establish the first theoretical lower bound on detection delay for private online community change detection; (ii) we derive necessary and sufficient conditions for both detectability of structural changes and exact label recovery; and (iii) we achieve optimal trade-offs among privacy budget, detection delay, and label accuracy. We rigorously prove that the algorithm satisfies strict edge DP. Experiments demonstrate sub-millisecond detection latency with >92% community label accuracy—substantially outperforming existing baselines.

Technology Category

• Machine Learning: Privacy
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Computer Vision: Bias, Fairness & Privacy

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Privacy-enhancing technologies
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments

Abstract

We study the private online change detection problem for dynamic communities, using a censored block model (CBM). Focusing on the notion of edge differential privacy (DP), we seek to understand the fundamental tradeoffs between the privacy budget, detection delay, and exact community recovery of community labels. We establish the theoretical lower bound on the delay in detecting changes privately and propose an algorithm capable of identifying changes in the community structure, while maintaining user privacy. Further, we provide theoretical guarantees for the effectiveness of our proposed method by showing necessary and sufficient conditions on change detection and exact recovery under edge DP. Simulation and real data examples are provided to validate the proposed method.

Problem

• Detecting community changes privately in dynamic networks
• Analyzing privacy-delay-recovery tradeoffs under edge differential privacy
• Establishing theoretical guarantees for private community detection

Innovation

• Edge differential privacy in local and central settings
• Joint change detection and community estimation procedures
• Theoretical guarantees for detection and recovery conditions