From dd3d7d5839a5086f84614d813d19f62b18f33e3e Mon Sep 17 00:00:00 2001 From: Justin Hsu Date: Mon, 28 Oct 2019 00:11:09 -0500 Subject: [PATCH] Fix. --- website/docs/schedule/lectures.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/website/docs/schedule/lectures.md b/website/docs/schedule/lectures.md index 610b698..0fd2377 100644 --- a/website/docs/schedule/lectures.md +++ b/website/docs/schedule/lectures.md @@ -16,12 +16,12 @@ 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 | 9/27 | Adversarial training
**Reading:** [*Towards Deep Learning Models Resistant to Adversarial Attacks*](https://arxiv.org/pdf/1706.06083.pdf)
**See also:** [*Ensemble Adversarial Training: Attacks and Defenses*](https://arxiv.org/pdf/1705.07204) | Siddhant/Goutham | Somya/Zi | |

**Applied Cryptography**

| | | -9/30 | Overview and basic constructions
**See also:** [Boneh and Shoup](https://crypto.stanford.edu/~dabo/cryptobook/BonehShoup_0_4.pdf), 11.6, 19.4
**See also:** [Evans, Kolesnikov, and Rosulek](https://securecomputation.org/), Chapter 3 | JH | --- | -10/2 | SMC for machine learning
**Reading:** [*Secure Computation for Machine Learning With SPDZ*](https://arxiv.org/pdf/1901.00329)
**Reading:** [*Helen: Maliciously Secure Coopetitive Learning for Linear Models*](https://arxiv.org/pdf/1907.07212) | Varun/Vibhor/Adarsh | --- | +9/30 | Overview and basic constructions
**Reading:** [Boneh and Shoup](https://crypto.stanford.edu/~dabo/cryptobook/BonehShoup_0_4.pdf), 11.6, 19.4
**See also:** [Evans, Kolesnikov, and Rosulek](https://securecomputation.org/), Chapter 3 | JH | --- | +10/2 | SMC for machine learning
**Reading:** [*Helen: Maliciously Secure Coopetitive Learning for Linear Models*](https://arxiv.org/pdf/1907.07212)
**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
**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
**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
**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
**Reading:** [*Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring*](https://arxiv.org/pdf/1802.04633) | Noor/Shashank | Joseph/Nils
**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
**Reading:** [*Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring*](https://arxiv.org/pdf/1802.04633)
**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 |

**Algorithmic Fairness**

| | | 10/14 | Overview and basic notions
**Reading:** [Barocas, Hardt, and Narayanan](https://fairmlbook.org/index.html), Chapter 1-2 | JH | --- | 10/16 | Individual and group fairness
**Reading:** [*Fairness through Awarness*](https://arxiv.org/pdf/1104.3913)
**Reading:** [*Equality of Opportunity in Supervised Learning*](https://arxiv.org/pdf/1610.02413) | JH | Jack/Jack |