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

arXiv: https://arxiv.org/abs/2405.05724 · HTML: https://mlsanity.com/paper/2405.05724/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2024.

## Built on by (most cited first, 3 of 3)

- [On the Price of Differential Privacy for Spectral Clustering over Stochastic Block Models](https://mlsanity.com/paper/2505.05816/) (2025)
- [Differentially Private Spectral Graph Clustering: Balancing Privacy, Accuracy, and Efficiency](https://mlsanity.com/paper/2510.07136/) (2025)
- [Locally Private Parametric Methods for Change-Point Detection](https://mlsanity.com/paper/2602.13619/) (2026)

## Abstract (arXiv)

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.