In this paper, we propose PrivGraph that exploits community information of the graph data to strike the trade-off between the perturbation noise and information loss.

The goal of differentially private analysis of graphs is to design algorithms that compute accurate global information about graphs while preserving privacy of individuals whose data is stored in …

In this tutorial, we will explore a set of diferentially private methods and techniques applicable to graph analytics, aiming to protect sensitive information and enable meaningful analysis in …

Jan 20, 2023 · This article provides a comprehensive survey of private graph data release algorithms that seek to achieve the fine balance between privacy and utility, with a specific …

Implementation of PrivGraph. The project contains 3 folders and 6 files. data (folder): All datasets are in this folder. comm (folder): This folder is used for community discovery. result (folder): …

In this paper, we study private sparsification of graphs. In particular, we give an algorithm that given an input graph, returns a sparse graph which approximates the spectrum of the input …

Mar 28, 2024 · This tutorial provides a comprehensive overview of differentially private methods and techniques to protect sensitive information while conducting meaningful graph analysis.

Aug 13, 2024 · 1.We propose a model for differentially private network data release, assuming common knowledge of graph topology but requiring protection of sensitive edge weights …

In this paper, we examine the trade-ofs between the accuracy and performance of various classes of diferentially private graph analysis algorithms by benchmarking them on real-world datasets.

Feb 11, 2024 · We study the applications of differential privacy (DP) in the context of graph-structured data and discuss the formulations of DP applicable to the publication of graphs and …