Node-Private Community Detection in Stochastic Block Models

TL;DR

This paper investigates the fundamental limits and algorithms for community detection in stochastic block models under strict node-level differential privacy, revealing a necessary logarithmic privacy budget for effective recovery.

Contribution

It introduces a node-private estimator using the exponential mechanism, establishes matching upper and lower bounds, and characterizes the privacy-accuracy tradeoff in SBM community detection.

Findings

Exact recovery is achievable with a logarithmic privacy budget.

Any pure node-private method requires at least a logarithmic privacy budget.

The paper provides a two-term minimax risk characterization balancing privacy and statistical signal.

Abstract

We study community detection in stochastic block models under pure node-level differential privacy, a stringent notion that protects the participation of an individual together with all of their incident edges. This setting is substantially more challenging than edge-private community detection, since modifying a single node can affect linearly many observations. On the algorithmic side, we analyze a node-private estimator based on the exponential mechanism combined with an extension lemma, and show that exact recovery remains achievable. In the standard sparse regime with logarithmic average degree and a fixed number of communities, our results imply that a logarithmic privacy budget suffices to obtain nontrivial recovery guarantees. On the lower bound side, we show that this logarithmic scaling is in fact unavoidable: any pure node-private method must fail to achieve polynomially small exact-recovery error, or polynomially small expected mismatch, unless the privacy budget is at least of this order. Moreover, in the regime of super-logarithmic privacy budgets, our upper and lower bounds yield a matching two-term characterization of the minimax risk, with one term governed by the non-private statistical signal and the other by the privacy budget; these match up to universal constants in the exponents. Taken together, our results identify an inherent logarithmic privacy cost in node-private community detection, absent under edge differential privacy, and provide a precise rate-level characterization of the tradeoff between node privacy and SBM recovery.