mlsanityData to 14 Oct 2026

A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications

arXiv 2308.16375 ↗First posted 31 Aug 2023cs.LG cs.AI cs.CRcited by 22

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Abstract (arXiv)

Graph Neural Networks (GNNs) have gained significant attention owing to their ability to handle graph-structured data and the improvement in practical applications. However, many of these models prioritize high utility performance, such as accuracy, with a lack of privacy consideration, which is a major concern in modern society where privacy attacks are rampant. To address this issue, researchers have started to develop privacy-preserving GNNs. Despite this progress, there is a lack of a comprehensive overview of the attacks and the techniques for preserving privacy in the graph domain. In this survey, we aim to address this gap by summarizing the attacks on graph data according to the targeted information, categorizing the privacy preservation techniques in GNNs, and reviewing the datasets and applications that could be used for analyzing/solving privacy issues in GNNs. We also outline potential directions for future research in order to build better privacy-preserving GNNs.

Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). Builds on 114 of its 166 references that are in the map. First posted on arXiv 31 Aug 2023.

Builds on

114 of 166 references.

References · 114

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38 more
  1. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,114
  2. 2019Deep Leakage from Gradientscited by 1,021
  3. 2018Representation Learning on Graphs with Jumping Knowledge Networkscited by 877
  4. 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 737
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  7. 2016Stealing Machine Learning Models via Prediction APIscited by 619
  8. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
  9. 2020Temporal Graph Networks for Deep Learning on Dynamic Graphscited by 530
  10. 2018ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Modelscited by 498
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  13. 2020Explainability in Graph Neural Networks: A Taxonomic Surveycited by 384
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  38. 2021Node-Level Membership Inference Attacks Against Graph Neural Networkscited by 84

50 most cited of 114 All 114 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 7; 2025: 8; 2026: 5.

22 papers.

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  1. 2024A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challengescited by 54
  2. 2024Graph Machine Learning in the Era of Large Language Models (LLMs)cited by 33
  3. 2024The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challengescited by 29
  4. 2024Safety in Graph Machine Learning: Threats and Safeguardscited by 11
  5. 2024GC4NC: A Benchmark Framework for Graph Condensation on Node Classification with New Insightscited by 7
  6. 2023Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systemscited by 6
  7. 2024Prompt-based Unifying Inference Attack on Graph Neural Networkscited by 3
  8. 2025Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defensescited by 3
  9. 2023Permutation-Invariant Graph Partitioning:How Graph Neural Networks Capture Structural Interactions?cited by 2
  10. 2025An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attackscited by 2
  11. 2026ATEX-CF: Attack-Informed Counterfactual Explanations for Graph Neural Networkscited by 2
  12. 2025Devil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocolscited by 1
10 more
  1. 2025DESIGN: Encrypted GNN Inference via Server-Side Input Graph Pruningcited by 1
  2. 2025FUSE: Fast Semi-Supervised Node Embedding Learning via Structural and Label-Aware Optimizationcited by 1
  3. 2024Vision Paper: Designing Graph Neural Networks in Compliance with the European Artificial Intelligence Actcited by 0
  4. 2025Fairness and/or Privacy on Social Graphscited by 0
  5. 2025Efficient and Privacy-Preserved Link Prediction via Condensed Graphscited by 0
  6. 2025Multi-View Graph Feature Propagation for Privacy Preservation and Feature Sparsitycited by 0
  7. 2026Adversarial Attacks on Locally Private Graph Neural Networkscited by 0
  8. 2026Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networkscited by 0
  9. 2026Target-Aware Interaction-Guided Reinforcement Learning for Black-Box Node Injection Attacks on Graph Neural Networkscited by 0
  10. 2026Auditing Privacy Risks in LLM-Enhanced Graph Neural Networkscited by 0