mlsanityData to 7 Oct 2026

Secure Deep Graph Generation with Link Differential Privacy

arXiv 2005.00455 ↗First posted 1 May 2020cs.SI cs.CR cs.LGcited by 24

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Abstract (arXiv)

Many data mining and analytical tasks rely on the abstraction of networks (graphs) to summarize relational structures among individuals (nodes). Since relational data are often sensitive, we aim to seek effective approaches to generate utility-preserved yet privacy-protected structured data. In this paper, we leverage the differential privacy (DP) framework to formulate and enforce rigorous privacy constraints on deep graph generation models, with a focus on edge-DP to guarantee individual link privacy. In particular, we enforce edge-DP by injecting proper noise to the gradients of a link reconstruction-based graph generation model, while ensuring data utility by improving structure learning with structure-oriented graph discrimination. Extensive experiments on two real-world network datasets show that our proposed DPGGAN model is able to generate graphs with effectively preserved global structure and rigorously protected individual link privacy.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 May 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 9; 2023: 4; 2024: 1; 2025: 5; 2026: 2.

24 papers.

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  1. 2021Subgraph Federated Learning with Missing Neighbor Generationcited by 132
  2. 2021Source Inference Attacks in Federated Learningcited by 31
  3. 2022Graph Auto-Encoder Via Neighborhood Wasserstein Reconstructioncited by 28
  4. 2022A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systemscited by 20
  5. 2022Exploiting Data Sparsity in Secure Cross-Platform Social Recommendationcited by 17
  6. 2024Deep Efficient Private Neighbor Generation for Subgraph Federated Learningcited by 14
  7. 2023Privacy-Preserving Graph Machine Learning from Data to Computation: A Surveycited by 10
  8. 2022A Privacy-Preserving Subgraph-Level Federated Graph Neural Network via Differential Privacycited by 7
  9. 2023Poincar'e Differential Privacy for Hierarchy-Aware Graph Embeddingcited by 7
  10. 2022Graph Generative Model for Benchmarking Graph Neural Networkscited by 6
  11. 2022Heterogeneous Graph Neural Network for Privacy-Preserving Recommendationcited by 6
  12. 2023Blink: Link Local Differential Privacy in Graph Neural Networks via Bayesian Estimationcited by 6
12 more
  1. 2021FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networkscited by 5
  2. 2022Cross-Network Social User Embedding with Hybrid Differential Privacy Guaranteescited by 5
  3. 2023Contrastive Learning for Non-Local Graphs with Multi-Resolution Structural Viewscited by 3
  4. 2022Heterogeneous Randomized Response for Differential Privacy in Graph Neural Networkscited by 2
  5. 2022Privacy-preserving Graph Analytics: Secure Generation and Federated Learningcited by 1
  6. 2025AdvSGM: Differentially Private Graph Learning via Adversarial Skip-gram Modelcited by 1
  7. 2025Structure-Preference Enabled Graph Embedding Generation under Differential Privacycited by 0
  8. 2025Adversarial Signed Graph Learning with Differential Privacycited by 0
  9. 2025CoPHo: Classifier-guided Conditional Topology Generation with Persistent Homologycited by 0
  10. 2025FairGFL: Privacy-Preserving Fairness-Aware Federated Learning with Overlapping Subgraphscited by 0
  11. 2026Taipan: A Query-free Transfer-based Multiple Sensitive Attribute Inference Attack Solely from Auxiliary Graphscited by 0
  12. 2026Differentially Private Modeling of Disease Transmission within Human Contact Networkscited by 0