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