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An awesome list of papers on privacy attacks against machine learning
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| 2026-07-31 | 640 |
| 2026-08-05 | 640 |
| 2026-08-06 | 640 |
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# Awesome Attacks on Machine Learning Privacy [](https://awesome.re) This repository contains a curated list of papers related to privacy attacks against machine learning. A code repository is provided when available by the authors. For corrections, suggestions, or missing papers, please either open an issue or submit a pull request. # Contents - [Awesome Attacks on Machine Learning Privacy ](#awesome-attacks-on-machine-learning-privacy-img-srchttpsawesomerebadgesvg-altawesome) - [Contents](#contents) - [Surveys and Overviews](#surveys-and-overviews) - [Privacy Testing Tools](#privacy-testing-tools) - [Papers and Code](#papers-and-code) - [Membership inference](#membership-inference) - [Reconstruction](#reconstruction) - [Property inference/Distribution inference](#property-inference) - [Model extraction](#model-extraction) - [Other](#other) # Surveys and Overviews - [**SoK: Model Inversion Attack Landscape: Taxonomy, Challenges, and Future Roadmap**](https://ieeexplore.ieee.org/abstract/document/10221914) (Sayanton Dibbo, 2023) - [**A Survey of Privacy Attacks in Machine Learning**](https://dl.acm.org/doi/10.1145/3624010) (Rigaki and Garcia, 2023) - [**An Overview of Privacy in Machine Learning**](https://arxiv.org/pdf/2005.08679) (De Cristofaro, 2020) - [**Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks**](https://arxiv.org/abs/2006.11601) (Fan et al., 2020) - [**Privacy and Security Issues in Deep Learning: A Survey**](https://ieeexplore.ieee.org/abstract/document/9294026) (Liu et al., 2021) - [**ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models**](https://arxiv.org/abs/2102.02551) (Liu et al., 2021) - [**Membership Inference Attacks on Machine Learning: A Survey**](https://arxiv.org/abs/2103.07853) (Hu et al., 2021) - [**Survey: Leakage and Privacy at Inference Time**](https://arxiv.org/abs/2107.01614) (Jegorova et al., 2021) - [**A Review of Confidentiality Threats Against Embedded Neural Network Models**](https://arxiv.org/abs/2105.01401) (Joud et al., 2021) - [**Federated Learning Attacks Revisited: A Critical Discussion of Gaps,Assumptions, and Evaluation Setups**](https://arxiv.org/abs/2111.03363) (Wainakh et al., 2021) - [**I Know What You Trained Last Summer: A Survey on Stealing Machine Learning Models and Defences**](https://arxiv.org/abs/2206.08451) (Oliynyk et al., 2022) # Privacy Testing Tools - [**PrivacyRaven**](https://github.com/trailofbits/PrivacyRaven) (Trail of Bits) - [**TensorFlow Privacy**](https://github.com/tensorflow/privacy/tree/master/tensorflow_privacy/privacy/membership_inference_attack) (TensorFlow) - [**Machine Learning Privacy Meter**](https://github.com/privacytrustlab/ml_privacy_meter) (NUS Data Privacy and Trustworthy Machine Learning Lab) - [**CypherCat (archive-only)**](https://github.com/Lab41/cyphercat) (IQT Labs/Lab 41) - [**Adversarial Robustness Toolbox (ART)**](https://github.com/Trusted-AI/adversarial-robustness-toolbox) (IBM) # Papers and Code ## Membership inference A curated list of membership inference papers (more than 100 papers) on machine learning models is available at [this repository](https://github.com/HongshengHu/membership-inference-machine-learning-literature). - [**Membership inference attacks against machine learning models**](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7958568) (Shokri et al., 2017) ([code](https://github.com/csong27/membership-inference)) - [**Understanding membership inferences on well-generalized learning models**](https://arxiv.org/pdf/1802.04889)(Long et al., 2018) - [**Privacy risk in machine learning: Analyzing the connection to overfitting**](https://ieeexplore.ieee.org/document/8429311), (Yeom et al., 2018) ([code](https://github.com/samuel-yeom/ml-privacy-csf18)) - [**Membership inference attack against differentially private deep learning model**](http://www.tdp
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:70aece5c9a0c0f45, topic:awesome, topic:awesome-list, desc:awesome list