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Add AML readings.

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The Spi Calculus*. Information and Computation, 1999.
### Adversarial Machine Learning
- Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru
Erhan, Ian Goodfellow, and Rob Fergus. [*Intriguing properties of neural
networks*](https://arxiv.org/pdf/1312.6199.pdf). ICLR 2014.
- Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. [*Explaining and
Harnessing Adversarial Examples*](https://arxiv.org/abs/1412.6572). ICLR 2015.
- Nicholas Carlini and David Wagner. [*Towards Evaluating the Robustness of
Neural Networks*](https://arxiv.org/pdf/1608.04644.pdf). S&P 2017.
- Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei
Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song. [*Robust Physical-World
Attacks on Deep Learning Models*](https://arxiv.org/pdf/1707.08945.pdf). CVPR 2018.
- Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and
Adrian Vladu. [*Towards Deep Learning Models Resistant to Adversarial
Attacks*](https://arxiv.org/pdf/1706.06083.pdf). ICLR 2018.
- Nicholas Carlini and David Wagner. [*Adversarial Examples Are Not Easily Detected:
Bypassing Ten Detection Methods*](https://arxiv.org/pdf/1705.07263.pdf). AISec 2017.
- Jacob Steinhardt, Pang Wei Koh, and Percy Liang. [*Certified Defenses for Data
Poisoning Attacks*](https://arxiv.org/pdf/1706.03691.pdf). NIPS 2017.
# Supplemental Material
- Cynthia Dwork and Aaron Roth. *Algorithmic Foundations of Data Privacy*.

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