7 papers
Private Approximation of Graph Spectra and Cuts via Spectral Amplifiers
Chenglin Fan, Jingcheng Liu, Pan Peng +2
We study the problem of releasing a synthetic graph that approximates the sizes of all cuts of an input graph under edge-level differential privacy. If one insists on purely additi…
Recovery thresholds for hidden weighted sparse graphs
Zhe Hou, Jingcheng Liu
Recovering structural information from noisy high-dimensional data is a fundamental task in statistical inference. We investigate the recovery thresholds for a graph hidden in a ra…
The Price of Privacy For Approximating Max-CSP
Prathamesh Dharangutte, Jingcheng Liu, Pasin Manurangsi +3
We study approximation algorithms for Maximum Constraint Satisfaction Problems (Max-CSPs) under differential privacy (DP) where the constraints are considered sensitive data. Infor…
Zero-free regions and concentration inequalities for hypergraph colorings in the local lemma regime
Jingcheng Liu, Yixiao Yu
We show that for -colorings in -uniform hypergraphs with maximum degree , if and , there is a "Lee-Yang" zero-free strip around the…
A Generalized Binary Tree Mechanism for Differentially Private Approximation of All-Pair Distances
Michael Dinitz, Chenglin Fan, Jingcheng Liu +2
We study the problem of approximating all-pair distances in a weighted undirected graph with differential privacy, introduced by Sealfon [Sea16]. Given a publicly known undirected…
Almost linear time differentially private release of synthetic graphs
Jingcheng Liu, Jalaj Upadhyay, Zongrui Zou
In this paper, we give an almost linear time and space algorithms to sample from an exponential mechanism with an -score function defined over an exponentially large non-co…