Unveiling Privacy Vulnerabilities: Investigating the Role of Structure in Graph Data

TL;DR

This paper investigates privacy risks in graph data related to network structure, introduces a new measure for privacy leakage, develops an inference attack, and proposes a privacy-preserving graph publishing method with strong experimental validation.

Contribution

It advances understanding of structural privacy risks in graphs, introduces the Generalized Homophily Ratio, and presents a novel privacy-preserving graph data publishing technique.

Findings

The attack model significantly threatens user privacy.

The proposed publishing method balances privacy and utility effectively.

Experimental results outperform baseline methods.

Abstract

The public sharing of user information opens the door for adversaries to infer private data, leading to privacy breaches and facilitating malicious activities. While numerous studies have concentrated on privacy leakage via public user attributes, the threats associated with the exposure of user relationships, particularly through network structure, are often neglected. This study aims to fill this critical gap by advancing the understanding and protection against privacy risks emanating from network structure, moving beyond direct connections with neighbors to include the broader implications of indirect network structural patterns. To achieve this, we first investigate the problem of Graph Privacy Leakage via Structure (GPS), and introduce a novel measure, the Generalized Homophily Ratio, to quantify the various mechanisms contributing to privacy breach risks in GPS. Based on this insight, we develop a novel graph private attribute inference attack, which acts as a pivotal tool for evaluating the potential for privacy leakage through network structures under worst-case scenarios. To protect users' private data from such vulnerabilities, we propose a graph data publishing method incorporating a learnable graph sampling technique, effectively transforming the original graph into a privacy-preserving version. Extensive experiments demonstrate that our attack model poses a significant threat to user privacy, and our graph data publishing method successfully achieves the optimal privacy-utility trade-off compared to baselines.