mlsanityData to 7 Oct 2026

Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey

arXiv 2307.04338 ↗First posted 10 Jul 2023cs.LG cs.CRcited by 10

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Abstract (arXiv)

In graph machine learning, data collection, sharing, and analysis often involve multiple parties, each of which may require varying levels of data security and privacy. To this end, preserving privacy is of great importance in protecting sensitive information. In the era of big data, the relationships among data entities have become unprecedentedly complex, and more applications utilize advanced data structures (i.e., graphs) that can support network structures and relevant attribute information. To date, many graph-based AI models have been proposed (e.g., graph neural networks) for various domain tasks, like computer vision and natural language processing. In this paper, we focus on reviewing privacy-preserving techniques of graph machine learning. We systematically review related works from the data to the computational aspects. We first review methods for generating privacy-preserving graph data. Then we describe methods for transmitting privacy-preserved information (e.g., graph model parameters) to realize the optimization-based computation when data sharing among multiple parties is risky or impossible. In addition to discussing relevant theoretical methodology and software tools, we also discuss current challenges and highlight several possible future research opportunities for privacy-preserving graph machine learning. Finally, we envision a unified and comprehensive secure graph machine learning system.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 55 of its 128 references that are in the map. First posted on arXiv 10 Jul 2023.

Builds on

55 of 128 references.

References · 55

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38 more
  1. 2020A Survey on Knowledge Graphs: Representation, Acquisition and Applicationscited by 643
  2. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  3. 2019Federated Learning with Personalization Layerscited by 533
  4. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 528
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  14. 2021Federated Graph Classification over Non-IID Graphscited by 113
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  16. 2021FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendationcited by 97
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50 most cited of 55 All 55 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 4; 2026: 4.

10 papers.

  1. 2025Towards Effective Federated Graph Foundation Model via Mitigating Knowledge Entanglementcited by 14
  2. 2024Gradient Compressed Sensing: A Query-Efficient Gradient Estimator for High-Dimensional Zeroth-Order Optimizationcited by 12
  3. 2025Joint Optimal Transport and Embedding for Network Alignmentcited by 10
  4. 2025FedBook: A Unified Federated Graph Foundation Codebook with Intra-domain and Inter-domain Knowledge Modelingcited by 8
  5. 2024Federated Continual Graph Learningcited by 5
  6. 2026OWLEYE: Zero-Shot Learner for Cross-Domain Graph Data Anomaly Detectioncited by 1
  7. 2026Generator-based Graph Generation via Heat Diffusioncited by 1
  8. 2025Enhancing Trust Management System for Connected Autonomous Vehicles Using Machine Learning Methods: A Surveycited by 0
  9. 2026Mitigating topology biases in Graph Diffusion via Counterfactual Interventioncited by 0
  10. 2026PriDyG: Privacy-preserving Dynamic Graph Inference with LLM-GNN Collaborationcited by 0