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 ## Privacy attacks on ML models:
 ### Membership inference attacks:
 Membership Inference Attacks against Machine Learning Models (2016): https://arxiv.org/abs/1610.05820
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 Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning (2018): https://ieeexplore.ieee.org/document/8835245
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 ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models (2018): https://arxiv.org/pdf/1806.01246.pdf
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 Stolen Memories: Leveraging Model Memorization for Calibrated White-Box Membership Inference (2020): https://arxiv.org/abs/1906.11798
 Label-Only Membership Inference Attacks (2020): https://arxiv.org/abs/2007.14321
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 Membership Inference Attacks on Machine Learning: A Survey (2021): http://arxiv.org/abs/2103.07853
 
 ### Attribute inference attacks:
 Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing (2014): https://www.usenix.org/system/files/conference/usenixsecurity14/sec14-paper-fredrikson-privacy.pdf
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 Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures (2015): https://rist.tech.cornell.edu/papers/mi-ccs.pdf
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 On the (In)Feasibility of Attribute Inference Attacks on Machine Learning Models (2021): https://arxiv.org/abs/2103.07101
 
 ### Additional privacy attacks/metrics:
 Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning (2019): https://arxiv.org/pdf/1904.01067.pdf
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 Towards Measuring Membership Privacy (2017): https://arxiv.org/abs/1712.09136
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 Modelling and Quantifying Membership Information Leakage in Machine Learning (2020): https://ui.adsabs.harvard.edu/abs/2020arXiv200110648F/abstract
 
 ## Risk assessment of ML models:
 Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting (2018): https://www.cs.cmu.edu/~mfredrik/papers/YeomCSF18.pdf
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 ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learning (2020): https://arxiv.org/abs/2007.09339
 
 ## Differential privacy for ML models:
 Deep Learning with Differential Privacy (2016): https://arxiv.org/abs/1607.00133
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 Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data (2016): https://arxiv.org/abs/1610.05755
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 Diffprivlib: The IBM Differential Privacy Library (2019): https://arxiv.org/abs/1907.02444
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 Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization (2020): https://arxiv.org/abs/2010.09063
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 A Survey on Differentially Private Machine Learning (2020): https://ieeexplore.ieee.org/document/9064731
 
 ## Trustworthy Machine Learning (list of papers and tools):