# Adversarial Attacks on Locally Private Graph Neural Networks

arXiv: https://arxiv.org/abs/2603.20746 · HTML: https://mlsanity.com/paper/2603.20746/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 38 references that are in the map.
- First posted on arXiv 21 Mar 2026.

## Builds on (most cited first, 21 of 21)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Adversarial Attacks on Neural Networks for Graph Data](https://mlsanity.com/paper/1805.07984/) (2018)
- [RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response](https://mlsanity.com/paper/1407.6981/) (2014)
- [Towards More Practical Adversarial Attacks on Graph Neural Networks](https://mlsanity.com/paper/2006.05057/) (2020)
- [TDGIA:Effective Injection Attacks on Graph Neural Networks](https://mlsanity.com/paper/2106.06663/) (2021)
- [Locally Private Graph Neural Networks](https://mlsanity.com/paper/2006.05535/) (2020)
- [Single Node Injection Attack against Graph Neural Networks](https://mlsanity.com/paper/2108.13049/) (2021)
- [Releasing Graph Neural Networks with Differential Privacy Guarantees](https://mlsanity.com/paper/2109.08907/) (2021)
- [Model Inversion Attacks against Graph Neural Networks](https://mlsanity.com/paper/2209.07807/) (2022)
- [A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications](https://mlsanity.com/paper/2308.16375/) (2023)
- [Adversarial Attack on Graph Neural Networks as An Influence Maximization Problem](https://mlsanity.com/paper/2106.10785/) (2021)
- [Private Graph Extraction via Feature Explanations](https://mlsanity.com/paper/2206.14724/) (2022)
- [NetFense: Adversarial Defenses against Privacy Attacks on Neural Networks for Graph Data](https://mlsanity.com/paper/2106.11865/) (2021)
- [Differentially Private Graph Classification with GNNs](https://mlsanity.com/paper/2202.02575/) (2022)
- [ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees](https://mlsanity.com/paper/2304.08928/) (2023)
- [Differentially Private Decoupled Graph Convolutions for Multigranular Topology Protection](https://mlsanity.com/paper/2307.06422/) (2023)
- [SecGNN: Privacy-Preserving Graph Neural Network Training and Inference as a Cloud Service](https://mlsanity.com/paper/2202.07835/) (2022)
- [GANI: Global Attacks on Graph Neural Networks via Imperceptible Node Injections](https://mlsanity.com/paper/2210.12598/) (2022)
- [Training Differentially Private Graph Neural Networks with Random Walk Sampling](https://mlsanity.com/paper/2301.00738/) (2023)
- [Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach](https://mlsanity.com/paper/2309.08569/) (2023)

## Abstract (arXiv)

Graph neural network (GNN) is a powerful tool for analyzing graph-structured data. However, their vulnerability to adversarial attacks raises serious concerns, especially when dealing with sensitive information. Local Differential Privacy (LDP) offers a privacy-preserving framework for training GNNs, but its impact on adversarial robustness remains underexplored. This paper investigates adversarial attacks on LDP-protected GNNs. We explore how the privacy guarantees of LDP can be leveraged or hindered by adversarial perturbations. The effectiveness of existing attack methods on LDP-protected GNNs are analyzed and potential challenges in crafting adversarial examples under LDP constraints are discussed. Additionally, we suggest directions for defending LDP-protected GNNs against adversarial attacks. This work investigates the interplay between privacy and security in graph learning, highlighting the need for robust and privacy-preserving GNN architectures.