Differentially Private All-Pairs Shortest Path Distances: Improved Algorithms and Lower Bounds
Justin Y. Chen, Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Shyam Narayanan, Jelani Nelson, Yinzhan Xu
Abstract
We study the problem of releasing the weights of all-pairs shortest paths in a weighted undirected graph with differential privacy (DP). In this setting, the underlying graph is fixed and two graphs are neighbors if their edge weights differ by at most 1 in the ℓ1-distance. We give an algorithm with additive error Õ(n2/3/ε) in the ε-DP case and an algorithm with additive error in the (ε,δ)-DP case, where n denotes the number of vertices. This positively answers a question of Sealfon [Sea16, Sea20], who asked whether a o(n)- error algorithm exists. We also show that an additive error of Ω(n1/6) is necessary for any sufficiently small ε,δ > 0. Furthermore, we show that if the graph is promised to have reasonably bounded weights, one can improve the error further to roughly in the ε-DP case and roughly in the (ε, δ)-DP case. Previously, it was only known how to obtain Õ(n2/3/ε1/3) additive error in the ε-DP case and additive error in the (ε,δ)-DP case for bounded-weight graphs [Sea16]. Finally, we consider a relaxation where a multiplicative approximation is allowed. We show that, with a multiplicative approximation factor k, the additive error can be reduced to Õ(n1/2+O(1/k)/ε) in the ε-DP case and Õ(n1/3+O(1/k)/ε) in the (ε,δ)-DP case.
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Install the CLIlune papers fulltext 6919dae3-6571-489b-bedd-43fdc862f872Cited by top-tier papers7
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