mlsanityData to 14 Oct 2026

Privacy-Utility Trade-offs in Neural Networks for Medical Population Graphs: Insights from Differential Privacy and Graph Structure

arXiv 2307.06760 ↗First posted 13 Jul 2023cs.LG cs.CRcited by 0

On the map: LLM safety and security → Membership inference attacks

Abstract (arXiv)

We initiate an empirical investigation into differentially private graph neural networks on population graphs from the medical domain by examining privacy-utility trade-offs at different privacy levels on both real-world and synthetic datasets and performing auditing through membership inference attacks. Our findings highlight the potential and the challenges of this specific DP application area. Moreover, we find evidence that the underlying graph structure constitutes a potential factor for larger performance gaps by showing a correlation between the degree of graph homophily and the accuracy of the trained model.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Membership inference attacks, one of 12 directions in LLM safety and security. Builds on 18 of its 27 references that are in the map. First posted on arXiv 13 Jul 2023.

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18 of 27 references.

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Same direction, newest first

  1. 2021Membership Inference Attacks From First Principles7 Dec

Other references · 17

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
  3. 2016Geometric deep learning: going beyond Euclidean datacited by 1,048
  4. 2019Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networkscited by 541
  5. 2019Does Learning Require Memorization? A Short Tale about a Long Tailcited by 460
  6. 2020Combining Label Propagation and Simple Models Out-performs Graph Neural Networkscited by 202
  7. 2020Stealing Links from Graph Neural Networkscited by 115
  8. 2021Node-Level Membership Inference Attacks Against Graph Neural Networkscited by 84
  9. 2020Quantifying Privacy Leakage in Graph Embeddingcited by 82
  10. 2021Membership Inference Attack on Graph Neural Networkscited by 81
  11. 2021LinkTeller: Recovering Private Edges from Graph Neural Networks via Influence Analysiscited by 65
  12. 2021Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implicationscited by 52
5 more
  1. 2021GraphMI: Extracting Private Graph Data from Graph Neural Networkscited by 50
  2. 2022GAP: Differentially Private Graph Neural Networks with Aggregation Perturbationcited by 46
  3. 2021Node-Level Differentially Private Graph Neural Networkscited by 43
  4. 2017Spectral Graph Convolutions for Population-based Disease Predictioncited by 42
  5. 2020Latent-Graph Learning for Disease Predictioncited by 39

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  1. 2021Releasing Graph Neural Networks with Differential Privacy Guarantees
  2. 2023Preserving Node-level Privacy in Graph Neural Networks
  3. 2022Degree-Preserving Randomized Response for Graph Neural Networks under Local Differential Privacy
  4. 2025DP-GPL: Differentially Private Graph Prompt Learning
  5. 2026Bayesian Membership Privacy for Graph Neural Networks
  6. 2026Impact of Graph Structure on Membership-Inference Risk for Graph Neural Networks
  7. 2023A Unified Framework of Graph Information Bottleneck for Robustness and Membership Privacy
  8. 2026Graph Reconstruction from Differentially Private GNN Explanations