A Differentially Private Clustering Algorithm for Well-Clustered Graphs

Weiqiang He, Hendrik Fichtenberger, Pan Peng

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Keywords: differential privacy, graph clustering, semidefinite programming, spectral clustering

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TL;DR: Differentially private spectral clustering that nearly matches non-private bounds

Abstract:

We study differentially private (DP) algorithms for recovering clusters in well-clustered graphs, which are graphs whose vertex set can be partitioned into a small number of sets, each inducing a subgraph of high inner conductance and small outer conductance. Such graphs have widespread application as a benchmark in the theoretical analysis of spectral clustering.
We provide an efficient ($\epsilon$,$\delta$)-DP algorithm tailored specifically for such graphs. Our algorithm draws inspiration from the recent work of Chen et al., who developed DP algorithms for recovery of stochastic block models in cases where the graph comprises exactly two nearly-balanced clusters. Our algorithm works for well-clustered graphs with $k$ nearly-balanced clusters, and the misclassification ratio almost matches the one of the best-known non-private algorithms. We conduct experimental evaluations on datasets with known ground truth clusters to substantiate the prowess of our algorithm. We also show that any (pure) $\epsilon$-DP algorithm would result in substantial error.

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Primary Area: societal considerations including fairness, safety, privacy

Submission Number: 7708