mlsanityData to 7 Oct 2026

Node Injection Link Stealing Attack

arXiv 2307.13548 ↗First posted 25 Jul 2023cs.CR cs.LGcited by 3

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Abstract (arXiv)

In this paper, we present a stealthy and effective attack that exposes privacy vulnerabilities in Graph Neural Networks (GNNs) by inferring private links within graph-structured data. Focusing on the inductive setting where new nodes join the graph and an API is used to query predictions, we investigate the potential leakage of private edge information. We also propose methods to preserve privacy while maintaining model utility. Our attack demonstrates superior performance in inferring the links compared to the state of the art. Furthermore, we examine the application of differential privacy (DP) mechanisms to mitigate the impact of our proposed attack, we analyze the trade-off between privacy preservation and model utility. Our work highlights the privacy vulnerabilities inherent in GNNs, underscoring the importance of developing robust privacy-preserving mechanisms for their application.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 23 of its 48 references that are in the map. First posted on arXiv 25 Jul 2023.

Builds on

23 of 48 references.

References · 23

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2018How Powerful are Graph Neural Networks?cited by 4,355
  5. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  6. 2018Link Prediction Based on Graph Neural Networkscited by 829
  7. 2019GraphSAINT: Graph Sampling Based Inductive Learning Methodcited by 618
  8. 2015Diffusion-Convolutional Neural Networkscited by 268
  9. 2021Enhanced Membership Inference Attacks against Machine Learning Modelscited by 237
  10. 2018Adversarial Attack and Defense on Graph Data: A Surveycited by 166
  11. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  12. 2020Stealing Links from Graph Neural Networkscited by 114
11 more
  1. 2020Quantifying Privacy Leakage in Graph Embeddingcited by 82
  2. 2021Membership Inference Attack on Graph Neural Networkscited by 81
  3. 2021LinkTeller: Recovering Private Edges from Graph Neural Networks via Influence Analysiscited by 65
  4. 2021Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implicationscited by 50
  5. 2021Twitch Gamers: a Dataset for Evaluating Proximity Preserving and Structural Role-based Node Embeddingscited by 48
  6. 2022GAP: Differentially Private Graph Neural Networks with Aggregation Perturbationcited by 46
  7. 2021Node-Level Differentially Private Graph Neural Networkscited by 43
  8. 2021A Hard Label Black-box Adversarial Attack Against Graph Neural Networkscited by 26
  9. 2022Label-Only Membership Inference Attack against Node-Level Graph Neural Networkscited by 18
  10. 2022Post-processing of Differentially Private Data: A Fairness Perspectivecited by 9
  11. 2022SoK: Differential Privacy on Graph-Structured Datacited by 9

Built on by

Cited by year

Citing papers per year: 2024: 2; 2025: 1.

3 papers.

  1. 2024Large Language Models for Link Stealing Attacks Against Graph Neural Networkscited by 3
  2. 2025Improving Epidemic Analyses with Privacy-Preserving Integration of Sensitive Datacited by 2
  3. 2024Large Language Models Merging for Enhancing the Link Stealing Attack on Graph Neural Networkscited by 0