Awesome

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/Distribution inference
  • Model extraction
• Other

Surveys and Overviews

• SoK: Model Inversion Attack Landscape: Taxonomy, Challenges, and Future Roadmap (Sayanton Dibbo, 2023)
• A Survey of Privacy Attacks in Machine Learning (Rigaki and Garcia, 2023)
• 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 and Security Issues in Deep Learning: A Survey (Liu et al., 2021)
• ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models (Liu et al., 2021)
• Membership Inference Attacks on Machine Learning: A Survey (Hu et al., 2021)
• Survey: Leakage and Privacy at Inference Time (Jegorova et al., 2021)
• A Review of Confidentiality Threats Against Embedded Neural Network Models (Joud et al., 2021)
• Federated Learning Attacks Revisited: A Critical Discussion of Gaps,Assumptions, and Evaluation Setups (Wainakh et al., 2021)
• I Know What You Trained Last Summer: A Survey on Stealing Machine Learning Models and Defences (Oliynyk et al., 2022)

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

A curated list of membership inference papers (more than 100 papers) on machine learning models is available at this repository.

• 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)
• Bootstrap Aggregation for Point-based Generalized Membership Inference Attacks (Felps et al., 2020)
• Differentially Private Learning Does Not Bound Membership Inference (Humphries et al., 2020)
• Quantifying Membership Privacy via Information Leakage (Saeidian et al., 2020)
• Disparate Vulnerability: on the Unfairness of Privacy Attacks Against Machine Learning (Yaghini et al., 2020)
• Use the Spear as a Shield: A Novel Adversarial Example based Privacy-Preserving Technique against Membership Inference Attacks (Xue et al., 2020)
• Towards Realistic Membership Inferences: The Case of Survey Data
• Unexpected Information Leakage of Differential Privacy Due to Linear Property of Queries (Huang et al., 2020)
• TransMIA: Membership Inference Attacks Using Transfer Shadow Training (Hidano et al., 2020)
• An Extension of Fano's Inequality for Characterizing Model Susceptibility to Membership Inference Attacks (Jha et al., 2020)
• Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning (Nasr et al., 2021)
• Membership Inference Attack with Multi-Grade Service Models in Edge Intelligence (Wang et al., 2021)
• Reconstruction-Based Membership Inference Attacks are Easier on Difficult Problems (Shafran et al., 2021)
• Membership Inference Attacks on Deep Regression Models for Neuroimaging (Gupta et al., 2021)
• Node-Level Membership Inference Attacks Against Graph Neural Networks (He et al., 2021)
• Practical Blind Membership Inference Attack via Differential Comparisons (Hui et al., 2021)
• ADePT: Auto-encoder based Differentially Private Text Transformation (Krishna et al., 2021)
• Source Inference Attacks in Federated Learning (Hu et al., 2021) (code)
• The Influence of Dropout on Membership Inference in Differentially Private Models (Galinkin, 2021)
• Membership Inference Attack Susceptibility of Clinical Language Models (Jagannatha et al., 2021)
• Membership Inference Attacks on Knowledge Graphs (Wang & Sun, 2021)
• When Does Data Augmentation Help With Membership Inference Attacks? (Kaya and Dumitras, 2021)
• The Influence of Training Parameters and Architectural Choices on the Vulnerability of Neural Networks to Membership Inference Attacks (Bouanani, 2021)
• Membership Inference on Word Embedding and Beyond (Mahloujifar et al., 2021)
• TableGAN-MCA: Evaluating Membership Collisions of GAN-Synthesized Tabular Data Releasing (Hu et al., 2021)
• Enhanced Membership Inference Attacks against Machine Learning Models (Ye et al., 2021)
• Do Not Trust Prediction Scores for Membership Inference Attacks (Hintersdorf et al., 2021)
• Membership Inference via Backdooring (Hu et al. 2022)

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)
• Trade-offs and Guarantees of Adversarial Representation Learning for Information Obfuscation (Zhao et al., 2020)
• 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)
• Black-box Model Inversion Attribute Inference Attacks on Classification Models (Mehnaz et al., 2020)
• Deep Face Recognizer Privacy Attack: Model Inversion Initialization by a Deep Generative Adversarial Data Space Discriminator (Khosravy et al., 2020)
• MixCon: Adjusting the Separability of Data Representations for Harder Data Recovery (Li et al., 2020)
• Evaluation of Inference Attack Models for Deep Learning on Medical Data (Wu et al., 2020)
• FaceLeaks: Inference Attacks against Transfer Learning Models via Black-box Queries (Liew and Takahashi, 2020)
• Extracting Training Data from Large Language Models (Carlini et al., 2020)
• MIDAS: Model Inversion Defenses Using an Approximate Memory System (Xu et al., 2021)
• KART: Privacy Leakage Framework of Language Models Pre-trained with Clinical Records (Nakamura et al., 2020)
• Derivation of Constraints from Machine Learning Models and Applications to Security and Privacy (Falaschi et al., 2021)
• On the (In)Feasibility of Attribute Inference Attacks on Machine Learning Models (Zhao et al., 2021)
• Practical Defences Against Model Inversion Attacks for Split Neural Networks (Titcombe et al., 2021)
• R-GAP: Recursive Gradient Attack on Privacy (Zhu and Blaschko, 2021)
• Exploiting Explanations for Model Inversion Attacks (Zhao et al., 2021)
• SAFELearn: Secure Aggregation for private FEderated Learning (Fereidooni et al., 2021)
• Does BERT Pretrained on Clinical Notes Reveal Sensitive Data? (Lehman et al., 2021)
• Training Data Leakage Analysis in Language Models (Inan et al., 2021)
• Model Fragmentation, Shuffle and Aggregation to Mitigate Model Inversion in Federated Learning (Masude et al., 2021)
• PRECODE - A Generic Model Extension to Prevent Deep Gradient Leakage (Scheliga et al., 2021)
• On the Importance of Encrypting Deep Features (Ni et al., 2021)
• Defending Against Model Inversion Attack by Adversarial Examples (Wen et al., 2021)
• See through Gradients: Image Batch Recovery via GradInversion (Yin et al., 2021)
• Variational Model Inversion Attacks (Wang et al., 2021)
• Reconstructing Training Data with Informed Adversaries (Balle et al., 2022)
• Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks (Struppek et al., 2022)
• Privacy Vulnerability of Split Computing to Data-Free Model Inversion Attacks (Dong et al., 2022)
• A Linear Reconstruction Approach for Attribute Inference Attacks against Synthetic Data (Annamalai et al., 2023)
• Analysis and Utilization of Hidden Information in Model Inversion Attacks (Zhang et al., 2023) (code)
• Text Embeddings Reveal (Almost) As Much As Text(Morris et al., 2023)
• On the Inadequacy of Similarity-based Privacy Metrics: Reconstruction Attacks against "Truly Anonymous Synthetic Data" (Ganev and De Cristofaro, 2023)
• Model Inversion Attack with Least Information and an In-depth Analysis of its Disparate Vulnerability (Dibbo et al., 2023)

Property inference / Distribution 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)
• Subject Property Inference Attack in Collaborative Learning (Xu and Li, 2020)
• Property Inference From Poisoning (Chase et al., 2021)
• Property Inference Attacks on Convolutional Neural Networks: Influence and Implications of Target Model's Complexity (Parisot et al., 2021)
• Honest-but-Curious Nets: Sensitive Attributes of Private Inputs can be Secretly Coded into the Entropy of Classifiers' Outputs (Malekzadeh et al. 2021) (code)
• Property Inference Attacks Against GANs (Zhou et al., 2021) (code)
• Formalizing and Estimating Distribution Inference Risks (Suri and Evans, 2022) (code)
• Dissecting Distribution Inference (Suri et al., 2023) (code)
• SNAP: Efficient Extraction of Private Properties with Poisoning (Chaudhari et al., 2023) (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)
• Model Extraction Attacks on Graph Neural Networks: Taxonomy and Realization (Wu et al., 2020)
• Model Extraction Attacks and Defenses on Cloud-Based Machine Learning Models (Gong et al., 2020)
• Leveraging Extracted Model Adversaries for Improved Black Box Attacks (Nizar and Kobren, 2020)
• Differentially Private Machine Learning Model against Model Extraction Attack (Cheng et al., 2020)
• Model Extraction Attacks and Defenses on Cloud-Based Machine Learning Models (Gong et al., 2020)
• Stealing Neural Network Models through the Scan Chain: A New Threat for ML Hardware (Potluri and Aysu, 2021)
• Model Extraction and Defenses on Generative Adversarial Networks (Hu and Pang, 2021)
• Protecting Decision Boundary of Machine Learning Model With Differentially Private Perturbation (Zheng et al., 2021)
• Special-Purpose Model Extraction Attacks: Stealing Coarse Model with Fewer Queries (Okada et al., 2021)
• Model Extraction and Adversarial Transferability, Your BERT is Vulnerable! (He et al., 2021) (code)
• Thief, Beware of What Get You There: Towards Understanding Model Extraction Attack (Zhang et al., 2021)
• Model Weight Theft With Just Noise Inputs: The Curious Case of the Petulant Attacker (Roberts et al., 2019)
• Protecting DNNs from Theft using an Ensemble of Diverse Models (Kariyappa et al., 2021)
• Information Laundering for Model Privacy (Wang et al., 2021)
• Deep Neural Network Fingerprinting by Conferrable Adversarial Examples (Lukas et al., 2021)
• BODAME: Bilevel Optimization for Defense Against Model Extraction (Mori et al., 2021)
• Dataset Inference: Ownership Resolution in Machine Learning (Maini et al., 2021)
• Good Artists Copy, Great Artists Steal: Model Extraction Attacks Against Image Translation Generative Adversarial Networks (Szyller et al., 2021)
• Towards Characterizing Model Extraction Queries and How to Detect Them (Zhang et al., 2021)
• Hardness of Samples Is All You Need: Protecting Deep Learning Models Using Hardness of Samples (Sadeghzadeh et al., 2021)
• Stateful Detection of Model Extraction Attacks (Pal et al., 2021)
• MEGEX: Data-Free Model Extraction Attack against Gradient-Based Explainable AI (Miura et al., 2021)
• INVERSENET: Augmenting Model Extraction Attacks with Training Data Inversion (Gong et al., 2021)
• Increasing the Cost of Model Extraction with Calibrated Proof of Work (Dziedzic et al. 2022) code
• On the Difficulty of Defending Self-Supervised Learning against Model Extraction (Dziedzic et al., 2022) code
• Dataset Inference for Self-Supervised Models (Dziedzic et al., 2022) code
• Can't Steal? Cont-Steal! Contrastive Stealing Attacks Against Image Encoders (Sha et al., 2022)
• StolenEncoder: Stealing Pre-trained Encoders (Liu et al., 2022)
• Model Extraction Attacks Revisited (Liang et al., 2023)

Other

• Prompts Should not be Seen as Secrets: Systematically Measuring Prompt Extraction Attack Success(Zhang et al., 2023)
• Amnesiac Machine Learning (Graves et al., 2020)
• 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)
• Synthetic Data -- Anonymisation Groundhog Day (Stadler et al., 2020) (code)
• Robust Membership Encoding: Inference Attacks and CopyrightProtection for Deep Learning (Song and Shokri, 2020)
• Quantifying Privacy Leakage in Graph Embedding (Duddu et al., 2020)
• Quantifying and Mitigating Privacy Risks of Contrastive Learning (He and Zhang, 2021)
• Coded Machine Unlearning (Aldaghri et al., 2020)
• Unlearnable Examples: Making Personal Data Unexploitable (Huang et al., 2021)
• Measuring Data Leakage in Machine-Learning Models with Fisher Information (Hannun et al., 2021)
• Teacher Model Fingerprinting Attacks Against Transfer Learning (Chen et al, 2021)
• Bounding Information Leakage in Machine Learning (Del Grosso et al., 2021)
• RoFL: Attestable Robustness for Secure Federated Learning (Burkhalter et al., 2021)
• Learning to Break Deep Perceptual Hashing: The Use Case NeuralHash (Struppek et al., 2021)
• The Privacy Onion Effect: Memorization is Relative (Carlini et al., 2022)
• Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets (Tramer et al., 2022)
• LCANets++: Robust Audio Classification using Multi-layer Neural Networks with Lateral Competition (Dibbo et al., 2023)