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Justin Hsu 2 years ago
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      website/docs/schedule/lectures.md

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website/docs/schedule/lectures.md

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9/25 | Certified defenses <br> **Reading:** [*Certified Defenses for Data Poisoning Attacks*](https://arxiv.org/pdf/1706.03691.pdf) <br> **Reading:** [*Certified Defenses against Adversarial Examples*](https://arxiv.org/pdf/1801.09344) | Joseph/Nils | Siddhant/Goutham |
9/27 | Adversarial training <br> **Reading:** [*Towards Deep Learning Models Resistant to Adversarial Attacks*](https://arxiv.org/pdf/1706.06083.pdf) <br> **See also:** [*Ensemble Adversarial Training: Attacks and Defenses*](https://arxiv.org/pdf/1705.07204) | Siddhant/Goutham | Somya/Zi |
| <center> <h4> **Applied Cryptography** </h4> </center> | | |
9/30 | Overview and basic constructions <br> **See also:** [Boneh and Shoup](https://crypto.stanford.edu/~dabo/cryptobook/BonehShoup_0_4.pdf), 11.6, 19.4 <br> **See also:** [Evans, Kolesnikov, and Rosulek](https://securecomputation.org/), Chapter 3 | JH | --- |
10/2 | SMC for machine learning <br> **Reading:** [*Secure Computation for Machine Learning With SPDZ*](https://arxiv.org/pdf/1901.00329) <br> **Reading:** [*Helen: Maliciously Secure Coopetitive Learning for Linear Models*](https://arxiv.org/pdf/1907.07212) | Varun/Vibhor/Adarsh | --- |
9/30 | Overview and basic constructions <br> **Reading:** [Boneh and Shoup](https://crypto.stanford.edu/~dabo/cryptobook/BonehShoup_0_4.pdf), 11.6, 19.4 <br> **See also:** [Evans, Kolesnikov, and Rosulek](https://securecomputation.org/), Chapter 3 | JH | --- |
10/2 | SMC for machine learning <br> **Reading:** [*Helen: Maliciously Secure Coopetitive Learning for Linear Models*](https://arxiv.org/pdf/1907.07212) <br> **See also:** [*Secure Computation for Machine Learning With SPDZ*](https://arxiv.org/pdf/1901.00329) | Varun/Vibhor/Adarsh | --- |
10/4 | Secure data collection at scale <br> **Reading:** [*Prio: Private, Robust, and Scalable Computation of Aggregate Statistics*](https://people.csail.mit.edu/henrycg/files/academic/papers/nsdi17prio.pdf) | Abhirav/Rajan | --- |
10/7 | Verifiable computing <br> **Reading:** [*SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud*](https://arxiv.org/pdf/1706.10268) | JH | --- |
10/9 | Side channels and implementation issues <br> **Reading:** [*On Significance of the Least Significant Bits For Differential Privacy*](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.366.5957&rep=rep1&type=pdf) | JH | --- |
10/11 | Model watermarking <br> **Reading:** [*Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring*](https://arxiv.org/pdf/1802.04633) | Noor/Shashank | Joseph/Nils <br> **See also:** [*Protecting Intellectual Property of Deep Neural Networks with Watermarking*](https://gzs715.github.io/pubs/WATERMARK_ASIACCS18.pdf) | MS1 Due
10/11 | Model watermarking <br> **Reading:** [*Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring*](https://arxiv.org/pdf/1802.04633) <br> **See also:** [*Protecting Intellectual Property of Deep Neural Networks with Watermarking*](https://gzs715.github.io/pubs/WATERMARK_ASIACCS18.pdf) | Noor/Shashank | Joseph/Nils| MS1 Due
| <center> <h4> **Algorithmic Fairness** </h4> </center> | | |
10/14 | Overview and basic notions <br> **Reading:** [Barocas, Hardt, and Narayanan](https://fairmlbook.org/index.html), Chapter 1-2 | JH | --- |
10/16 | Individual and group fairness <br> **Reading:** [*Fairness through Awarness*](https://arxiv.org/pdf/1104.3913) <br> **Reading:** [*Equality of Opportunity in Supervised Learning*](https://arxiv.org/pdf/1610.02413) | JH | Jack/Jack |

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