{"total":51,"rows":[{"id":"2204.08570","title":"A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability","created":"2022-04-18","cited_by_count":118,"area":null},{"id":"2102.05429","title":"Node-Level Membership Inference Attacks Against Graph Neural Networks","created":"2021-02-10","cited_by_count":84,"area":null},{"id":"2403.04468","title":"A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges","created":"2024-03-07","cited_by_count":54,"area":null},{"id":"2110.08760","title":"Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications","created":"2021-10-17","cited_by_count":52,"area":null},{"id":"2203.00949","title":"GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation","created":"2022-03-02","cited_by_count":46,"area":null},{"id":"2302.13406","title":"GNNDelete: A General Strategy for Unlearning in Graph Neural Networks","created":"2023-02-26","cited_by_count":43,"area":null},{"id":"2109.08907","title":"Releasing Graph Neural Networks with Differential Privacy Guarantees","created":"2021-09-18","cited_by_count":32,"area":null},{"id":"2308.16375","title":"A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications","created":"2023-08-31","cited_by_count":22,"area":null},{"id":"2205.10014","title":"A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection","created":"2022-05-20","cited_by_count":20,"area":null},{"id":"2207.13766","title":"Label-Only Membership Inference Attack against Node-Level Graph Neural Networks","created":"2022-07-27","cited_by_count":19,"area":null},{"id":"2105.01798","title":"Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence","created":"2021-05-04","cited_by_count":18,"area":null},{"id":"2201.09398","title":"Towards Private Learning on Decentralized Graphs with Local Differential Privacy","created":"2022-01-23","cited_by_count":16,"area":null},{"id":"2206.14724","title":"Private Graph Extraction via Feature Explanations","created":"2022-06-29","cited_by_count":16,"area":null},{"id":"2304.08566","title":"GrOVe: Ownership Verification of Graph Neural Networks using Embeddings","created":"2023-04-17","cited_by_count":16,"area":null},{"id":"2306.08604","title":"A Unified Framework of Graph Information Bottleneck for Robustness and Membership Privacy","created":"2023-06-14","cited_by_count":14,"area":null},{"id":"2104.06523","title":"A Review of Anonymization for Healthcare Data","created":"2021-04-13","cited_by_count":12,"area":null},{"id":"2202.02575","title":"Differentially Private Graph Classification with GNNs","created":"2022-02-05","cited_by_count":12,"area":null},{"id":"2304.08928","title":"ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees","created":"2023-04-18","cited_by_count":12,"area":null},{"id":"2411.10023","title":"Model Inversion Attacks: A Survey of Approaches and Countermeasures","created":"2024-11-15","cited_by_count":11,"area":null},{"id":"2203.09205","title":"SoK: Differential Privacy on Graph-Structured Data","created":"2022-03-17","cited_by_count":10,"area":null},{"id":"2312.07861","title":"GraphGuard: Detecting and Counteracting Training Data Misuse in Graph Neural Networks","created":"2023-12-13","cited_by_count":8,"area":null},{"id":"2405.03911","title":"Federated Graph Condensation with Information Bottleneck Principles","created":"2024-05-07","cited_by_count":8,"area":null},{"id":"2306.00578","title":"Does Black-box Attribute Inference Attacks on Graph Neural Networks Constitute Privacy Risk?","created":"2023-06-01","cited_by_count":7,"area":null},{"id":"2312.12183","title":"Poincar\\'e Differential Privacy for Hierarchy-Aware Graph Embedding","created":"2023-12-19","cited_by_count":7,"area":null},{"id":"2207.10896","title":"Privacy and Transparency in Graph Machine Learning: A Unified Perspective","created":"2022-07-22","cited_by_count":5,"area":null},{"id":"2301.00738","title":"Training Differentially Private Graph Neural Networks with Random Walk Sampling","created":"2023-01-02","cited_by_count":5,"area":null},{"id":"2308.01469","title":"VertexSerum: Poisoning Graph Neural Networks for Link Inference","created":"2023-08-02","cited_by_count":5,"area":null},{"id":"2309.08569","title":"Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach","created":"2023-09-15","cited_by_count":5,"area":null},{"id":"2511.01023","title":"Seed-Induced Uniqueness in Transformer Models: Subspace Alignment Governs Subliminal Transfer","created":"2025-11-02","cited_by_count":5,"area":null},{"id":"2308.08360","title":"Independent Distribution Regularization for Private Graph Embedding","created":"2023-08-16","cited_by_count":4,"area":null},{"id":"2501.10985","title":"GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models","created":"2025-01-19","cited_by_count":4,"area":null},{"id":"2505.02566","title":"Robustness questions the interpretability of graph neural networks: what to do?","created":"2025-05-05","cited_by_count":4,"area":null},{"id":"2506.00808","title":"Unlearning Inversion Attacks for Graph Neural Networks","created":"2025-06-01","cited_by_count":4,"area":null},{"id":"2307.13548","title":"Node Injection Link Stealing Attack","created":"2023-07-25","cited_by_count":3,"area":null},{"id":"2308.13513","title":"Unveiling the Role of Message Passing in Dual-Privacy Preservation on GNNs","created":"2023-08-25","cited_by_count":3,"area":null},{"id":"2405.05784","title":"Link Stealing Attacks Against Inductive Graph Neural Networks","created":"2024-05-09","cited_by_count":3,"area":null},{"id":"2412.15735","title":"Prompt-based Unifying Inference Attack on Graph Neural Networks","created":"2024-12-20","cited_by_count":3,"area":null},{"id":"2508.04894","title":"Adversarial Attacks and Defenses on Graph-aware Large Language Models (LLMs)","created":"2025-08-06","cited_by_count":3,"area":null},{"id":"2508.19641","title":"Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defenses","created":"2025-08-27","cited_by_count":3,"area":null},{"id":"2210.11730","title":"Privacy-Preserved Neural Graph Similarity Learning","created":"2022-10-21","cited_by_count":2,"area":null},{"id":"2406.02027","title":"Inference Attacks: A Taxonomy, Survey, and Promising Directions","created":"2024-06-04","cited_by_count":2,"area":null},{"id":"2503.19070","title":"Graph-Level Label-Only Membership Inference Attack against Graph Neural Networks","created":"2025-03-24","cited_by_count":2,"area":null},{"id":"2311.03075","title":"SoK: Memorisation in machine learning","created":"2023-11-06","cited_by_count":1,"area":null},{"id":"2308.04943","title":"Differentially Private Graph Neural Network with Importance-Grained Noise Adaption","created":"2023-08-09","cited_by_count":0,"area":null},{"id":"2411.02809","title":"Query-Efficient Adversarial Attack Against Vertical Federated Graph Learning","created":"2024-11-05","cited_by_count":0,"area":null},{"id":"2507.19964","title":"Who Owns This Sample: Cross-Client Membership Inference Attack in Federated Graph Neural Networks","created":"2025-07-26","cited_by_count":0,"area":null},{"id":"2602.00407","title":"Fed-Listing: Federated Label Distribution Inference in Graph Neural Networks","created":"2026-01-30","cited_by_count":0,"area":null},{"id":"2602.06700","title":"Taipan: A Query-free Transfer-based Multiple Sensitive Attribute Inference Attack Solely from Auxiliary Graphs","created":"2026-02-06","cited_by_count":0,"area":null},{"id":"2605.01987","title":"Misclassification Rate and Privacy-Utility Trade-offs in Graph Convolutional Networks via Subsampling Stability","created":"2026-05-03","cited_by_count":0,"area":null},{"id":"2605.31427","title":"DG-CoLearn: An Efficient Collaborative Learning Framework for Dynamic Graphs","created":"2026-05-29","cited_by_count":0,"area":null},{"id":"2608.25727","title":"Auditing Privacy Risks in LLM-Enhanced Graph Neural Networks","created":"2026-08-26","cited_by_count":0,"area":null}],"next_cursor":null}