mlsanityData to 14 Oct 2026

Does Black-box Attribute Inference Attacks on Graph Neural Networks Constitute Privacy Risk?

arXiv 2306.00578 ↗First posted 1 Jun 2023cs.LG cs.AI cs.CRcited by 7

Not on the map. Why?

Abstract (arXiv)

Graph neural networks (GNNs) have shown promising results on real-life datasets and applications, including healthcare, finance, and education. However, recent studies have shown that GNNs are highly vulnerable to attacks such as membership inference attack and link reconstruction attack. Surprisingly, attribute inference attacks has received little attention. In this paper, we initiate the first investigation into attribute inference attack where an attacker aims to infer the sensitive user attributes based on her public or non-sensitive attributes. We ask the question whether black-box attribute inference attack constitutes a significant privacy risk for graph-structured data and their corresponding GNN model. We take a systematic approach to launch the attacks by varying the adversarial knowledge and assumptions. Our findings reveal that when an attacker has black-box access to the target model, GNNs generally do not reveal significantly more information compared to missing value estimation techniques. Code is available.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 25 of its 30 references that are in the map. First posted on arXiv 1 Jun 2023.

Builds on

25 of 30 references.

References · 25

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2019Graph Neural Networks for Social Recommendationcited by 714
  3. 2020Graph Neural Networks in Recommender Systems: A Surveycited by 494
  4. 2018Adversarial Attacks on Neural Networks for Graph Datacited by 397
  5. 2018Adversarial Attack on Graph Structured Datacited by 301
  6. 2020Backdoor Attacks to Graph Neural Networkscited by 128
  7. 2021Towards a Unified Framework for Fair and Stable Graph Representation Learningcited by 128
  8. 2020Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Modelscited by 116
  9. 2020Stealing Links from Graph Neural Networkscited by 115
  10. 2018Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Diseasecited by 99
  11. 2020Quantifying Privacy Leakage in Graph Embeddingcited by 82
  12. 2021Membership Inference Attack on Graph Neural Networkscited by 81
13 more
  1. 2021On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Featurescited by 63
  2. 2019Attacking Graph-based Classification via Manipulating the Graph Structurecited by 53
  3. 2021GraphMI: Extracting Private Graph Data from Graph Neural Networkscited by 50
  4. 2021Model Stealing Attacks Against Inductive Graph Neural Networkscited by 50
  5. 2022Are Attribute Inference Attacks Just Imputation?cited by 49
  6. 2022Are Your Sensitive Attributes Private? Novel Model Inversion Attribute Inference Attacks on Classification Modelscited by 43
  7. 2020Model Extraction Attacks on Graph Neural Networks: Taxonomy and Realizationcited by 40
  8. 2021Graph Embedding for Recommendation against Attribute Inference Attackscited by 40
  9. 2021Releasing Graph Neural Networks with Differential Privacy Guaranteescited by 32
  10. 2022Model Inversion Attacks against Graph Neural Networkscited by 26
  11. 2022Private Graph Extraction via Feature Explanationscited by 16
  12. 2021A Review of Anonymization for Healthcare Datacited by 12
  13. 2021MuCoMiD: A Multitask Convolutional Learning Framework for miRNA-Disease Association Predictioncited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 3; 2026: 1.

7 papers.

  1. 2024A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challengescited by 54
  2. 2023A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applicationscited by 22
  3. 2024Prompt-based Unifying Inference Attack on Graph Neural Networkscited by 3
  4. 2025Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defensescited by 3
  5. 2025Fairness and/or Privacy on Social Graphscited by 0
  6. 2025GraphToxin: Reconstructing Full Unlearned Graphs from Graph Unlearningcited by 0
  7. 2026Taipan: A Query-free Transfer-based Multiple Sensitive Attribute Inference Attack Solely from Auxiliary Graphscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2021Node-Level Membership Inference Attacks Against Graph Neural Networks
  2. 2026Impact of Graph Structure on Membership-Inference Risk for Graph Neural Networks
  3. 2021Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications
  4. 2024Graph Transductive Defense: a Two-Stage Defense for Graph Membership Inference Attacks
  5. 2026Inference Attacks Against Graph Generative Diffusion Models
  6. 2026Bayesian Membership Privacy for Graph Neural Networks
  7. 2023A Unified Framework of Graph Information Bottleneck for Robustness and Membership Privacy
  8. 2025An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks