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awesome-ml-privacy-attacks
An awesome list of papers on privacy attacks against machine learning
https://github.com/stratosphereips/awesome-ml-privacy-attacks
- Awesome Attacks on Machine Learning Privacy )
- **Towards reverse-engineering black-box neural networks.**
- **Knockoff nets: Stealing functionality of black-box models**
- **PRADA: protecting against DNN model stealing attacks** - protecting-against-dnn-model-stealing-attacks))
- **Model Reconstruction from Model Explanations**
- **Exploring connections between active learning and model extraction**
- **High Accuracy and High Fidelity Extraction of Neural Networks**
- **Thieves on Sesame Street! Model Extraction of BERT-based APIs** - research/language/tree/master/language/bert_extraction))
- **Cryptanalytic Extraction of Neural Network Models**
- **CloudLeak: Large-Scale Deep Learning Models Stealing Through Adversarial Examples**
- **ACTIVETHIEF: Model Extraction Using Active Learning and Unannotated Public Data**
- **Efficiently Stealing your Machine Learning Models**
- **Extraction of Complex DNN Models: Real Threat or Boogeyman?**
- **Stealing Neural Networks via Timing Side Channels**
- **DeepSniffer: A DNN Model Extraction Framework Based on Learning Architectural Hints**
- **CSI NN: Reverse Engineering of Neural Network Architectures Through Electromagnetic Side Channel**
- **Cache Telepathy: Leveraging Shared Resource Attacks to Learn DNN Architectures**
- **How to 0wn NAS in Your Spare Time** - Hong/How-to-0wn-NAS-in-Your-Spare-Time))
- **Security Analysis of Deep Neural Networks Operating in the Presence of Cache Side-Channel Attacks**
- **Reverse-Engineering Deep ReLU Networks**
- **Model Extraction Oriented Data Publishing with k-anonymity**
- **Hermes Attack: Steal DNN Models with Lossless Inference Accuracy**
- **Model extraction from counterfactual explanations**
- **MetaSimulator: Simulating Unknown Target Models for Query-Efficient Black-box Attacks**
- **Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks** - poisoning))
- **IReEn: Iterative Reverse-Engineering of Black-Box Functions via Neural Program Synthesis**
- **ES Attack: Model Stealing against Deep Neural Networks without Data Hurdles**
- **Black-Box Ripper: Copying black-box models using generative evolutionary algorithms** - box-ripper))
- **Model Extraction Attacks on Graph Neural Networks: Taxonomy and Realization**
- **Model Extraction Attacks and Defenses on Cloud-Based Machine Learning Models**
- **Leveraging Extracted Model Adversaries for Improved Black Box Attacks**
- **Differentially Private Machine Learning Model against Model Extraction Attack**
- **Model Extraction Attacks and Defenses on Cloud-Based Machine Learning Models**
- **Stealing Neural Network Models through the Scan Chain: A New Threat for ML Hardware**
- **Model Extraction and Defenses on Generative Adversarial Networks**
- **Protecting Decision Boundary of Machine Learning Model With Differentially Private Perturbation**
- **Special-Purpose Model Extraction Attacks: Stealing Coarse Model with Fewer Queries**
- **Model Extraction and Adversarial Transferability, Your BERT is Vulnerable!**
- **Thief, Beware of What Get You There: Towards Understanding Model Extraction Attack**
- **Model Weight Theft With Just Noise Inputs: The Curious Case of the Petulant Attacker**
- **Protecting DNNs from Theft using an Ensemble of Diverse Models**
- **Information Laundering for Model Privacy**
- **Deep Neural Network Fingerprinting by Conferrable Adversarial Examples**
- **BODAME: Bilevel Optimization for Defense Against Model Extraction**
- **Dataset Inference: Ownership Resolution in Machine Learning**
- **Good Artists Copy, Great Artists Steal: Model Extraction Attacks Against Image Translation Generative Adversarial Networks**
- **Towards Characterizing Model Extraction Queries and How to Detect Them**
- **Hardness of Samples Is All You Need: Protecting Deep Learning Models Using Hardness of Samples**
- **Stateful Detection of Model Extraction Attacks**
- **MEGEX: Data-Free Model Extraction Attack against Gradient-Based Explainable AI**
- **INVERSENET: Augmenting Model Extraction Attacks with Training Data Inversion**
- **Increasing the Cost of Model Extraction with Calibrated Proof of Work** - lab/model-extraction-iclr)
- **On the Difficulty of Defending Self-Supervised Learning against Model Extraction** - lab/ssl-attacks-defenses)
- **Dataset Inference for Self-Supervised Models** - lab/DatasetInferenceForSelfSupervisedModels)
- **Can't Steal? Cont-Steal! Contrastive Stealing Attacks Against Image Encoders**
- **StolenEncoder: Stealing Pre-trained Encoders**
- **Model Extraction Attacks Revisited**
- **Prompts Should not be Seen as Secrets: Systematically Measuring Prompt Extraction Attack Success**
- **Amnesiac Machine Learning**
- **Toward Robustness and Privacy in Federated Learning: Experimenting with Local and Central Differential Privacy**
- **Analyzing Information Leakage of Updates to Natural Language Models**
- **Estimating g-Leakage via Machine Learning**
- **Information Leakage in Embedding Models**
- **Hide-and-Seek Privacy Challenge**
- **Synthetic Data -- Anonymisation Groundhog Day** - epfl/synthetic_data_release))
- **Robust Membership Encoding: Inference Attacks and CopyrightProtection for Deep Learning**
- **Quantifying Privacy Leakage in Graph Embedding**
- **Quantifying and Mitigating Privacy Risks of Contrastive Learning**
- **Coded Machine Unlearning**
- **Unlearnable Examples: Making Personal Data Unexploitable**
- **Measuring Data Leakage in Machine-Learning Models with Fisher Information**
- **Teacher Model Fingerprinting Attacks Against Transfer Learning**
- **Bounding Information Leakage in Machine Learning**
- **RoFL: Attestable Robustness for Secure Federated Learning**
- **Learning to Break Deep Perceptual Hashing: The Use Case NeuralHash**
- **The Privacy Onion Effect: Memorization is Relative**
- **Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets**
- **LCANets++: Robust Audio Classification using Multi-layer Neural Networks with Lateral Competition**
Programming Languages
Keywords
machine-learning
3
privacy
3
python
2
inference
2
deep-learning
1
membership-inference
1
model-extraction
1
model-inversion
1
privacy-enhancing-technologies
1
privacy-preserving-machine-learning
1
data-privacy
1
data-protection
1
data-protection-impact-assessment
1
explainable-ai
1
gdpr
1
information-leakage
1
membership-inference-attack
1
privacy-audit
1
adversarial-attacks
1
adversarial-examples
1
adversarial-machine-learning
1
ai
1
artificial-intelligence
1
attack
1
blue-team
1
evasion
1
extraction
1
poisoning
1
red-team
1
trusted-ai
1
trustworthy-ai
1