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9/13 | Differentially private machine learning
**Reading:** [*On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches*](https://arxiv.org/pdf/1708.08022)
**Reading:** [*Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data*](https://arxiv.org/pdf/1610.05755) | Robert/Shengwen | Zach/Jialu |
|
**Adversarial Machine Learning**
| |
9/16 | Overview and basic concepts | JH | --- |
-9/18 | Adversarial examples
**Reading:** [*Intriguing Properties of Neural Networks*](https://arxiv.org/pdf/1312.6199.pdf)
**Reading:** [*Explaining and Harnessing Adversarial Examples*](https://arxiv.org/abs/1412.6572)
**Reading:** [*Robust Physical-World Attacks on Deep Learning Models*](https://arxiv.org/pdf/1707.08945.pdf) | JH | Robert/Shengwen |
+9/18 | Adversarial examples
**Reading:** [*Intriguing Properties of Neural Networks*](https://arxiv.org/pdf/1312.6199.pdf)
**Reading:** [*Explaining and Harnessing Adversarial Examples*](https://arxiv.org/pdf/1412.6572)
**Reading:** [*Robust Physical-World Attacks on Deep Learning Models*](https://arxiv.org/pdf/1707.08945.pdf) | JH | Robert/Shengwen |
9/20 | Data poisoning
**Reading:** [*Poisoning Attacks against Support Vector Machines*](https://arxiv.org/pdf/1206.6389)
**Reading:** [*Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks*](https://arxiv.org/pdf/1804.00792) | Somya/Zi | Miru/Pierre |
9/23 | Defenses and detection: challenges
**Reading:** [*Towards Evaluating the Robustness of Neural Networks*](https://arxiv.org/pdf/1608.04644.pdf)
**Reading:** [*Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods*](https://arxiv.org/pdf/1705.07263.pdf) | JH | --- |
9/25 | Certified defenses
**Reading:** [*Certified Defenses for Data Poisoning Attacks*](https://arxiv.org/pdf/1706.03691.pdf)
**Reading:** [*Certified Defenses against Adversarial Examples*](https://arxiv.org/pdf/1801.09344) | Joseph/Nils | Siddhant/Goutham |