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Differentially Private Gomory-Hu Trees

NeurIPS 2025 · Federated learning and privacy › Differential privacy · approach: Differentially private clustering · 0 citations (OpenAlex)

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Gives an epsilon-DP polynomial-time algorithm that outputs an approximate Gomory-Hu tree with tilde-O(n/eps) additive error for all-pairs min s-t cuts, via a new DP min isolating cuts primitive.

Problem

Releasing all-pairs minimum s-t cuts (APMC) in an undirected weighted graph under differential privacy with small additive error; prior bounds were O(n^{3/2}/eps) pure DP and sqrt(mn)/eps approximate DP.

Approach

Recursive Gomory-Hu construction (low-depth, Steiner-tree variant) where each min-isolating-cut step is privatized using a DP min s-t cut with penalty edges to a sink, a changing acceptance threshold across sampling levels, and noisy edges added to contracted instances so that each edge influences only one recursive branch, with budget split across levels.

Key results

Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use a pure-DP approximate Gomory-Hu tree instead of releasing a synthetic graph that preserves all cutsAPMC · APMCadditive error: O(n^{3/2}/\eps) -> \Tilde{O}(n/\eps)Helped
Use a pure-DP approximate Gomory-Hu tree instead of the approximate-DP all-cuts algorithmAPMC · APMCadditive error: \tilde{O}(\sqrt{mn}/\eps) -> \Tilde{O}(n/\eps)Helped
Introduce a private Min Isolating Cuts algorithmDifferentially private Gomory-Hu tree · Min Isolating Cutsadditive error: \tO(n/\eps)Helped
Recover global Min Cut from the private Gomory-Hu treeDifferentially private Gomory-Hu tree · Global Min Cutadditive error: \tilde{O}(n/\eps)Helped
Use the private Gomory-Hu tree to construct a minimum k-cutDifferentially private Gomory-Hu tree · Minimum k-cutadditive error: \tilde{O}(k^{1.5}/\eps) -> \tilde{O}(nk/\eps)Mixed

Surprising findings

Limitations

Experimental setup

Metrics: additive error of min s-t cut values approximation of Gomory-Hu tree

Baselines: Gupta-Roth-Ullman all-cuts synthetic graph (O(n^{3/2}/eps)) Liu-Upadhyay-Zou synthetic graph (sqrt(mn)/eps, approx DP) Laplace noise on all cut values (O(n/eps), approx DP)

Builds on: Gomory-Hu tree (Abboud et al. low-depth recursion) Dalirrooyfard et al. private min s-t cut Gupta-Roth-Ullman private cuts Isolating cuts lemma

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