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# Materials
* Cynthia Dwork and Aaron Roth. Algorithmic Foundations of Differential Privacy.
* Dwork and Roth. [Algorithmic Foundations of Differential Privacy](https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf).
* Boneh and Shoup. [A Graduate Course in Applied Cryptography](https://crypto.stanford.edu/~dabo/cryptobook/).
* Evans, Kolesnikov, and Rosulek. [A Pragmatic Introduction to Secure Multi-Party Computation](https://securecomputation.org/).
* Barocas, Hardt, and Narayanan. [Fairness and Machine Learning](https://fairmlbook.org/).
# Other courses
* CIS 800 (UPenn): https://www.cis.upenn.edu/~aaroth/courses/privacyF11.html
* CS 229r (Harvard): https://cseweb.ucsd.edu/~dstefan/cse291-winter18/
* CSE 291 (UCSD): https://cseweb.ucsd.edu/~dstefan/cse291-winter18/
- CSE 291: [Language-Based Security](https://cseweb.ucsd.edu/~dstefan/cse291-winter18/) (Deian Stefan, UC San Diego)
- CSE 711: [Topics in Differential Privacy](https://www.acsu.buffalo.edu/~gaboardi/teaching/CSE711-spring16.html) (Marco Gaboardi, University at Buffalo)
- CS 800: [The Algorithmic Foundations of Data Privacy](https://www.cis.upenn.edu/~aaroth/courses/privacyF11.html) (Aaron Roth, UPenn)
- CS 229r: [Mathematical Approaches to Data Privacy](http://people.seas.harvard.edu/~salil/diffprivcourse/spring13/) (Salil Vadhan, Harvard)
- CS 294: [Fairness in Machine Learning](https://fairmlclass.github.io/) (Moritz Hardt, UC Berkeley)
- CS 598: [Special Topics in Adversarial Machine Learning](http://www.crystal-boli.com/teaching.html) (Bo Li, UIUC)
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