Author

Jingcheng Liu

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Jul 2026

Private Approximation of Graph Spectra and Cuts via Spectral Amplifiers

A primitive with a sharper error dependence on $n$ and $d$ for the downstream cut approximation is developed and combined with a new edge-sensitive terminal cut oracle with additive error yields the final worst-case polynomial-time private cut-release error.

Chenglin Fan, Jingcheng Liu, Pan Peng et al. · arXiv.org · 0 citations

2025

A Generalized Binary Tree Mechanism for Private Approximation of All-Pair Shortest Distances

The problem of approximating all-pair distances in a weighted undirected graph with differential privacy is studied, and efficient algorithms with significantly improved bounds are obtained on a broad class of graphs which are referred to as recursively separable.

Zongrui Zou, Chenglin Fan, Michael Dinitz et al. · Advances in Neural Informati... · 0 citations