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.

Contents

• Awesome Attacks on Machine Learning Privacy
• Contents
• Surveys and Overviews
• Privacy Testing Tools
• Papers and Code
  • Membership inference
  • Reconstruction
  • Property inference
  • Model extraction
• Other

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)

Privacy Testing Tools

• PrivacyRaven (Trail of Bits)
• TensorFlow Privacy (TensorFlow)
• Machine Learning Privacy Meter (NUS Data Privacy and Trustworthy Machine Learning Lab)
• CypherCat (archive-only) (IQT Labs/Lab 41)
• Adversarial Robustness Toolbox (ART) (IBM)

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 et al., 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)
• 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)
• Stolen Memories: Leveraging Model Memorization for Calibrated White-Box Membership Inference (Leino and Fredrikson, 2020)
• Label-Only Membership Inference Attacks (Choquette Choo et al., 2020)
• Label-Leaks: Membership Inference Attack with Label (Li and Zhang, 2020)
• Alleviating Privacy Attacks via Causal Learning (Tople et al., 2020)
• On the Effectiveness of Regularization Against Membership Inference Attacks (Kaya et al., 2020)
• Sampling Attacks: Amplification of Membership Inference Attacks by Repeated Queries (Rahimian et al., 2020)
• Segmentations-Leak: Membership Inference Attacks and Defenses in Semantic Image Segmentation (He et al., 2019)
• Differential Privacy Defenses and Sampling Attacks for Membership Inference (Rahimian et al., 2019)
• privGAN: Protecting GANs from membership inference attacks at low cost (Mukherjee et al., 2020)
• Sharing Models or Coresets: A Study based on Membership Inference Attack (Lu et al., 2020)
• Privacy Analysis of Deep Learning in the Wild: Membership Inference Attacks against Transfer Learning (Zou et al., 2020)
• Quantifying Membership Inference Vulnerability via Generalization Gap and Other Model Metrics (Bentley et al., 2020)
• MACE: A Flexible Framework for Membership Privacy Estimation in Generative Models (Liu et al., 2020)
• On Primes, Log-Loss Scores and (No) Privacy (Aggarwal et al., 2020)
• MCMIA: Model Compression Against Membership Inference Attack in Deep Neural Networks (Wang et al., 2020)

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)
• Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning (Salem et al., 2019)
• Illuminating the Dark or how to recover what should not be seen in FE-based classifiers (Carpov et al., 2020)
• Evaluation Indicator for Model Inversion Attack (Tanaka et al., 2020)
• Understanding Unintended Memorization in Federated Learning (Thakkar et al., 2020)
• An Attack-Based Evaluation Method for Differentially Private Learning Against Model Inversion Attack (Park et al., 2019)
• Reducing Risk of Model Inversion Using Privacy-Guided Training (Goldsteen et al., 2020)
• Robust Transparency Against Model Inversion Attacks (Alufaisan et al., 2020)
• Does AI Remember? Neural Networks and the Right to be Forgotten (Graves et al., 2020)
• Improving Robustness to Model Inversion Attacks via Mutual Information Regularization (Wang et al., 2020)
• SAPAG: A Self-Adaptive Privacy Attack From Gradients (Wang et al., 2020)
• Theory-Oriented Deep Leakage from Gradients via Linear Equation Solver (Pan et al., 2020)
• Improved Techniques for Model Inversion Attacks (Chen et al., 2020)

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)
• 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) (code)
• Efficiently Stealing your Machine Learning Models (Reith 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) (code)
• CSI NN: Reverse Engineering of Neural Network Architectures Through Electromagnetic Side Channel (Batina et al., 2019)
• Cache Telepathy: Leveraging Shared Resource Attacks to Learn DNN Architectures (Yan et al., 2020)
• How to 0wn NAS in Your Spare Time (Hong et al., 2020) (code)
• Security Analysis of Deep Neural Networks Operating in the Presence of Cache Side-Channel Attacks (Hong et al., 2020)
• Reverse-Engineering Deep ReLU Networks (Rolnick and Kording, 2020)
• Model Extraction Oriented Data Publishing with k-anonymity (Fukuoka et al., 2020)
• Hermes Attack: Steal DNN Models with Lossless Inference Accuracy (Zhu et al., 2020)
• Model extraction from counterfactual explanations (Aïvodji et al., 2020) (code)
• MetaSimulator: Simulating Unknown Target Models for Query-Efficient Black-box Attacks (Chen and Yong, 2020) (code)
• Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks (Orekondy et al., 2019) (code)
• IReEn: Iterative Reverse-Engineering of Black-Box Functions via Neural Program Synthesis (Hajipour et al., 2020)
• ES Attack: Model Stealing against Deep Neural Networks without Data Hurdles (Yuan et al., 2020)
• Black-Box Ripper: Copying black-box models using generative evolutionary algorithms (Barbalau et al., 2020) (code)

Other

• Toward Robustness and Privacy in Federated Learning: Experimenting with Local and Central Differential Privacy (Naseri et al., 2020)
• Analyzing Information Leakage of Updates to Natural Language Models (Brockschmidt et al., 2020)
• Estimating g-Leakage via Machine Learning (Romanelli et al., 2020)
• Information Leakage in Embedding Models (Song and Raghunathan, 2020)
• Hide-and-Seek Privacy Challenge (Jordan et al., 2020)