Differentially Private Densest Subgraph Detection

TL;DR

This paper introduces the first differentially private algorithms for densest subgraph detection in private networks, balancing privacy and accuracy with proven guarantees and empirical validation on real-world data.

Contribution

It presents the first sequential and parallel differentially private algorithms for densest subgraph detection with additive approximation guarantees.

Findings

Algorithms perform well on high-density networks

Good privacy-accuracy tradeoff observed in experiments

First to address privacy in densest subgraph detection

Abstract

Densest subgraph detection is a fundamental graph mining problem, with a large number of applications. There has been a lot of work on efficient algorithms for finding the densest subgraph in massive networks. However, in many domains, the network is private, and returning a densest subgraph can reveal information about the network. Differential privacy is a powerful framework to handle such settings. We study the densest subgraph problem in the edge privacy model, in which the edges of the graph are private. We present the first sequential and parallel differentially private algorithms for this problem. We show that our algorithms have an additive approximation guarantee. We evaluate our algorithms on a large number of real-world networks, and observe a good privacy-accuracy tradeoff when the network has high density.