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Differentially Private Gomory-Hu Trees

Abstract

Given an undirected, weighted nn-vertex graph G=(V,E,w)G = (V, E, w), a Gomory-Hu tree TT is a weighted tree on VV such that for any pair of distinct vertices s,tVs, t \in V, the Min-ss-tt-Cut on TT is also a Min-ss-tt-Cut on GG. Computing a Gomory-Hu tree is a well-studied problem in graph algorithms and has received considerable attention. In particular, a long line of work recently culminated in constructing a Gomory-Hu tree in almost linear time [Abboud, Li, Panigrahi and Saranurak, FOCS 2023]. We design a differentially private (DP) algorithm that computes an approximate Gomory-Hu tree. Our algorithm is ε\varepsilon-DP, runs in polynomial time, and can be used to compute ss-tt cuts that are O~(n/ε)\tilde{O}(n/\varepsilon)-additive approximations of the Min-ss-tt-Cuts in GG for all distinct s,tVs, t \in V with high probability. Our error bound is essentially optimal, as [Dalirrooyfard, Mitrovi\'c and Nevmyvaka, NeurIPS 2023] showed that privately outputting a single Min-ss-tt-Cut requires Ω(n)\Omega(n) additive error even with (1,0.1)(1, 0.1)-DP and allowing for a multiplicative error term. Prior to our work, the best additive error bounds for approximate all-pairs Min-ss-tt-Cuts were O(n3/2/ε)O(n^{3/2}/\varepsilon) for ε\varepsilon-DP [Gupta, Roth and Ullman, TCC 2012] and O(mnpolylog(n/δ)/ε)O(\sqrt{mn} \cdot \text{polylog}(n/\delta) / \varepsilon) for (ε,δ)(\varepsilon, \delta)-DP [Liu, Upadhyay and Zou, SODA 2024], both of which are implied by differential private algorithms that preserve all cuts in the graph. An important technical ingredient of our main result is an ε\varepsilon-DP algorithm for computing minimum Isolating Cuts with O~(n/ε)\tilde{O}(n / \varepsilon) additive error, which may be of independent interest.

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