Local Differential Privacy-Preserving Spectral Clustering for General Graphs

TL;DR

This paper analyzes the stability of spectral clustering under local differential privacy for general graphs, showing that edge flipping with certain probabilities preserves clustering results, unlike in SBM-generated graphs.

Contribution

It extends the analysis of differential privacy in spectral clustering from SBM to general graphs, providing theoretical bounds and empirical validation.

Findings

Edge flipping with probability O(log n/n) preserves clustering stability.

Spectral clustering can be unstable on certain well-clustered graphs under higher flipping probabilities.

The optimal privacy budget for general graphs is Theta(log n).

Abstract

Spectral clustering is a widely used algorithm to find clusters in networks. Several researchers have studied the stability of spectral clustering under local differential privacy with the additional assumption that the underlying networks are generated from the stochastic block model (SBM). However, we argue that this assumption is too restrictive since social networks do not originate from the SBM. Thus, we delve into an analysis for general graphs in this work. Our primary focus is the edge flipping method -- a common technique for protecting local differential privacy. We show that, when the edges of an $n$-vertex graph satisfying some reasonable well-clustering assumptions are flipped with a probability of $O(\log n/n)$, the clustering outcomes are largely consistent. Empirical tests further corroborate these theoretical findings. Conversely, although clustering outcomes have been stable for non-sparse and well-clustered graphs produced from the SBM, we show that in general, spectral clustering may yield highly erratic results on certain well-clustered graphs when the flipping probability is $\omega(\log n/n)$. This indicates that the best privacy budget obtainable for general graphs is $\Theta(\log n)$.