NetFense: Adversarial Defenses against Privacy Attacks on Neural Networks for Graph Data

TL;DR

NetFense is a novel graph perturbation method designed to defend against GNN-based privacy attacks by maintaining data utility and reducing private label inference, effectively balancing privacy and utility in graph data.

Contribution

This work introduces a new adversarial defense approach, NetFense, that perturbs graph data to protect node privacy while preserving utility for targeted label classification.

Findings

Effective privacy protection against GNN attacks

Maintains high utility for targeted label prediction

Preserves local neighborhood structures

Abstract

Recent advances in protecting node privacy on graph data and attacking graph neural networks (GNNs) gain much attention. The eye does not bring these two essential tasks together yet. Imagine an adversary can utilize the powerful GNNs to infer users' private labels in a social network. How can we adversarially defend against such privacy attacks while maintaining the utility of perturbed graphs? In this work, we propose a novel research task, adversarial defenses against GNN-based privacy attacks, and present a graph perturbation-based approach, NetFense, to achieve the goal. NetFense can simultaneously keep graph data unnoticeability (i.e., having limited changes on the graph structure), maintain the prediction confidence of targeted label classification (i.e., preserving data utility), and reduce the prediction confidence of private label classification (i.e., protecting the privacy of nodes). Experiments conducted on single- and multiple-target perturbations using three real graph data exhibit that the perturbed graphs by NetFense can effectively maintain data utility (i.e., model unnoticeability) on targeted label classification and significantly decrease the prediction confidence of private label classification (i.e., privacy protection). Extensive studies also bring several insights, such as the flexibility of NetFense, preserving local neighborhoods in data unnoticeability, and better privacy protection for high-degree nodes.