Proceedings
Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete Algorithms

Differentially Private Release of Synthetic Graphs

Abstract

We propose a (ϵ, δ)-differentially private mechanism that, given an input graph G with n vertices and m edges, in polynomial time generates a synthetic graph G’ approximating all cuts of the input graph up to an additive error of . This is the first construction of differentially private cut approximator that allows additive error o(m) for all m > n logC n. The best known previous results gave additive O(n3/2) error and hence only retained information about the cut structure on very dense graphs. Thus, we are making a notable progress on a promiment problem in differential privacy. We also present lower bounds showing that our utility/privacy trade-off is essentially the best possible if one seeks to get purely additive cut approximations.

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cover image Proceedings
Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete Algorithms
Pages: 560 - 578
Editor: Shuchi Chawla
ISBN (Online): 978-1-611975-99-4

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Published online: 23 December 2019

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