Awesome-model-inversion-attack

Survey

Arxiv 2022 - A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability. [paper]

Arxiv 2022 - Trustworthy Graph Neural Networks: Aspects, Methods and Trends. [paper]

Arxiv 2022 - A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection. [paper]

Graph Learning

IJCAI 2021 - GraphMI: Extracting Private Graph Data from Graph Neural Networks. [paper] [code]

WWW 2022 - Learning Privacy-Preserving Graph Convolutional Network with Partially Observed Sensitive Attributes. [paper]

Arxiv 2022 - DIFFERENTIALLY PRIVATE GRAPH CLASSIFICATION WITH GNNS. [paper]

Arxiv 2022 - GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation. [paper]

Arxiv 2022 - SOK: DIFFERENTIAL PRIVACY ON GRAPH-STRUCTURED DATA. [paper]

Arxiv 2022 - Degree-Preserving Randomized Response for Graph Neural Networks under Local Differential Privacy. [paper]

Arxiv 2022 - Private Graph Extraction via Feature Explanations. [paper]