Created Relevant papers (markdown)

Relevant-papers.md

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+## ML-guided anonymization paper:
+https://arxiv.org/abs/2007.13086
+
+## Privacy attacks on ML models:
+### Membership inference attacks:
+Membership Inference Attacks against Machine Learning Models (2016): https://arxiv.org/abs/1610.05820
+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
+ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models (2018): https://arxiv.org/pdf/1806.01246.pdf
+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
+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
+Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures (2015): https://rist.tech.cornell.edu/papers/mi-ccs.pdf
+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
+Towards Measuring Membership Privacy (2017): https://arxiv.org/abs/1712.09136
+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
+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
+Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data (2016): https://arxiv.org/abs/1610.05755
+Diffprivlib: The IBM Differential Privacy Library (2019): https://arxiv.org/abs/1907.02444
+Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization (2020): https://arxiv.org/abs/2010.09063
+A Survey on Differentially Private Machine Learning (2020): https://ieeexplore.ieee.org/document/9064731
+
+## Trustworthy Machine Learning (list of papers and tools):
+https://trustworthy-machine-learning.github.io/