Awesome-model-inversion-attack

Survey

Arxiv 2021 - A Survey of Privacy Attacks in Machine Learning. [paper]

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]

Computer Vision

CCS 2015 - Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures. [paper] [code1] [code2] [code3]

CSF 2016 - A Methodology for Formalizing Model-Inversion Attacks. [paper]

Arxiv 2019 - Adversarial Neural Network Inversion via Auxiliary Knowledge Alignment. [paper]

CVPR 2020 - The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks. [paper] [code]

APSIPA ASC 2020 - Deep Face Recognizer Privacy Attack: Model Inversion Initialization by a Deep Generative Adversarial Data Space Discriminator. [paper]

Arxiv 2020 - Neural Network Inversion in Adversarial Setting via Background Knowledge Alignment. [paper] [code]

NeurIPS 2021 - Variational Model Inversion Attacks. [paper] [code]

ICCV 2021 - Exploiting Explanations for Model Inversion Attacks. [paper]

ICCV 2021 - Knowledge-Enriched Distributional Model Inversion Attacks. [paper] [code]

AAAI 2021 - Improving Robustness to Model Inversion Attacks via Mutual Information Regularization. [paper]

CVPR 2022 - Label-Only Model Inversion Attacks via Boundary Repulsion. [paper] [code]

KDD 2022 - Bilateral Dependency Optimization: Defending Against Model-inversion Attacks. [paper] [code]

USENIX Security 2022 - ML-DOCTOR: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models. [paper] [code]

Arxiv 2022 - Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks. [paper] [code]

Graph Learning

USENIX Security 2020 - Stealing Links from Graph Neural Networks. [paper]

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

Arxiv 2021 - Node-Level Membership Inference Attacks Against Graph Neural Networks. [paper]

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

USENIX Security 2022 - Inference Attacks Against Graph Neural Networks [paper] [code]

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]

Others

2021 - ML and DP. [slides]