Awesome Attacks on Machine Learning Privacy

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.

Surveys and Overviews

• A Survey of Privacy Attacks in Machine Learning (Rigaki and Garcia, 2020)
• An Overview of Privacy in Machine Learning (De Cristofaro, 2020)
• Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks (Fan et al., 2020)

Papers and Code

Membership inference

• Membership inference attacks against machine learning models (Shokri et al., 2017) (code)
• Understanding membership inferences on well-generalized learning models(Long et al., 2018)
• Privacy risk in machine learning: Analyzing the connection to overfitting, (Yeom et al., 2018) (code)
• Membership inference attack against differentially private deep learning model (Rahman et al., 2018)
• Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning. (Nasr et al., 2019) (code)
• Logan: Membership inference attacks against generative models. (Hayes et al. 2019) (code)
• Evaluating differentially private machine learning in practice (Jayaraman and Evans, 2019) (code)
• Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models (Salem et al., 2019) (code)
• Privacy risks of securing machine learning models against adversarial examples (Song L. et al., 2019) (code)
• White-box vs Black-box: Bayes Optimal Strategies for Membership Inference (Sablayrolles etal., 2019)
• Privacy risks of explaining machine learning models (Shokri et al., 2019)
• Demystifying membership inference attacks in machine learning as a service (Truex et al., 2019)
• Monte carlo and reconstruction membership inference attacks against generative models (Hilprecht et al., 2019)
• MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples (Jia et al., 2019) (code)
• Gan-leaks: A taxonomy of membership inference attacks against gans (Chen,et al., 2019))
• Auditing Data Provenance in Text-Generation Models (Song and Shmatikov, 2019)
• Membership Inference Attacks on Sequence-to-Sequence Models: Is My Data In Your Machine Translation System? (Hisamoto et al., 2020)
• Revisiting Membership Inference Under Realistic Assumptions (Jayaraman et al., 2020)
• When Machine Unlearning Jeopardizes Privacy (Chen et al., 2020)
• Modelling and Quantifying Membership Information Leakage in Machine Learning (Farokhi and Kaafar, 2020)
• Privacy Risks of Securing Machine Learning Models against Adversarial Examples (Song et al., 2019) (code)
• Systematic Evaluation of Privacy Risks of Machine Learning Models (Song and Mittal, 2020) (code)
• Towards the Infeasibility of Membership Inference on Deep Models (Rezaei and Liu, 2020) (code)

Reconstruction

Reconstruction attacks cover also attacks known as model inversion and attribute inference.

• Privacy in pharmacogenetics: An end-to-end case study of personalized warfarin dosing (Fredrikson et al., 2014)
• Model inversion attacks that exploit confidence information and basic countermeasures (Fredrikson et al., 2015) (code)
• A methodology for formalizing model-inversion attacks (Wu et al., 2016)
• Deep models under the gan: Information leakage from collaborative deep learning (Hitaj et al., 2017)
• Machine learning models that remember too much (Song, C. et al., 2017) (code)
• Model inversion attacks for prediction systems: Without knowledge of non-sensitive attributes (Hidano et al., 2017)
• The secret sharer: Evaluating and testing unintended memorization in neural networks (Carlini et al., 2019)
• Deep leakage from gradients (Zhu et al., 2019) (code)
• Model inversion attacks against collaborative inference (He et al., 2019) (code)
• Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning (Wang et al., 2019)
• Neural network inversion in adversarial setting via background knowledge alignment (Yang et al., 2019)
• iDLG: Improved Deep Leakage from Gradients (Zhao et al., 2020) (code)
• Privacy Risks of General-Purpose Language Models (Pan et al., 2020)
• The secret revealer: generative model-inversion attacks against deep neural networks) (Zhang et al., 2020)
• Inverting Gradients - How easy is it to break privacy in federated learning? (Geiping et al., 2020)
• GAMIN: An Adversarial Approach to Black-Box Model Inversion (Aivodji et al., 2019)
• Adversarial Privacy Preservation under Attribute Inference Attack (Zhao et al., 2019)
• Reconstruction of training samples from loss functions (Sannai, 2018)
• A Framework for Evaluating Gradient Leakage Attacks in Federated Learning (Wei et al., 2020)
• Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning (Hitaj et al., 2017)
• Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning (Wang et al., 2018)
• Exploring Image Reconstruction Attack in Deep Learning Computation Offloading (Oh and Lee, 2019)
• I Know What You See: Power Side-Channel Attack on Convolutional Neural Network Accelerators (Wei et al., 2019)

Property inference

• Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers (Ateniese et al., 2015)
• Property inference attacks on fully connected neural networks using permutation invariant representations (Ganju et al., 2018)
• Exploiting unintended feature leakage in collaborative learning (Melis et al., 2019) (code)
• Overlearning Reveals Sensitive Attributes (Song C. et al., 2020) (code)

Model extraction

• Stealing machine learning models via prediction apis (Tramèr et al., 2016) (code) (code)
• Stealing hyperparameters in machine learning(Wang B. et al., 2018)
• Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Data (Correia-Silva et al., 2018) (code)
• Towards reverse-engineering black-box neural networks.(Oh et al., 2018) (code)
• Knockoff nets: Stealing functionality of black-box models (Orekondy et al., 2019) (code)
• PRADA: protecting against DNN model stealing attacks (Juuti et al., 2019) (code)
• Model Reconstruction from Model Explanations (Milli et al., 2019)
• Exploring connections between active learning and model extraction (Chandrasekaran et al., 2020)
• High Accuracy and High Fidelity Extraction of Neural Networks (Jagielski et al., 2020)
• Thieves on Sesame Street! Model Extraction of BERT-based APIs (Krishna et al., 2020) (code)
• Cryptanalytic Extraction of Neural Network Models (Carlini et al., 2020)
• CloudLeak: Large-Scale Deep Learning Models Stealing Through Adversarial Examples (Yu et al., 2020)
• ACTIVETHIEF: Model Extraction Using Active Learning and Unannotated Public Data (Pal et al., 2020)
• Efficiently Stealing your Machine Learning Models (Reith et al., 2019)
• A framework for the extraction of Deep Neural Networks by leveraging public data (Pal et al., 2019)
• Extraction of Complex DNN Models: Real Threat or Boogeyman? (Atli et al., 2020)
• Stealing Neural Networks via Timing Side Channels (Duddu et al., 2019)
• DeepSniffer: A DNN Model Extraction Framework Based on Learning Architectural Hints (Hu et al., 2020)

Privacy Metrics and Estimations

• Estimating g-Leakage via Machine Learning (Romanelli et al., 2020)

Tools

Coming Soon