Update readings.

website/docs/schedule/lectures.md

@@ -10,7 +10,7 @@
 9/13  | Differentially private machine learning <br> **Reading:** [*On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches*](https://arxiv.org/pdf/1708.08022) <br> **Reading:** [*Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data*](https://arxiv.org/pdf/1610.05755) | Robert/Shengwen | Zach/Jialu |
       | <center> <h4> **Adversarial Machine Learning** </h4> </center> | |
 9/16  | Overview and basic concepts | JH | --- |
-9/18  | Adversarial examples <br> **Reading:** [*Intriguing Properties of Neural Networks*](https://arxiv.org/pdf/1312.6199.pdf) <br> **Reading:** [*Explaining and Harnessing Adversarial Examples*](https://arxiv.org/pdf/1412.6572) <br> **Reading:** [*Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples*](https://arxiv.org/pdf/1605.07277.pdf) | JH | Robert/Shengwen |
+9/18  | Adversarial examples <br> **Reading:** [*Intriguing Properties of Neural Networks*](https://arxiv.org/pdf/1312.6199.pdf) <br> **Reading:** [*Explaining and Harnessing Adversarial Examples*](https://arxiv.org/pdf/1412.6572) | JH | Robert/Shengwen |
 9/20  | Data poisoning <br> **Reading:** [*Poisoning Attacks against Support Vector Machines*](https://arxiv.org/pdf/1206.6389) <br> **Reading:** [*Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks*](https://arxiv.org/pdf/1804.00792) | Somya/Zi | Miru/Pierre |
 9/23  | Defenses and detection: challenges <br> **Reading:** [*Towards Evaluating the Robustness of Neural Networks*](https://arxiv.org/pdf/1608.04644.pdf) <br> **Reading:** [*Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods*](https://arxiv.org/pdf/1705.07263.pdf) | JH | --- |
 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 |
@@ -18,7 +18,7 @@
       | <center> <h4> **Applied Cryptography** </h4> </center> | | |
 9/30  | Overview and basic constructions | 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 | --- |
-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) <br> **Reading:** [*RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response*](https://arxiv.org/pdf/1407.6981.pdf) | Abhirav/Rajan | --- |
+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:** [*Protecting Intellectual Property of Deep Neural Networks with Watermarking*](https://gzs715.github.io/pubs/WATERMARK_ASIACCS18.pdf) <br> **Reading:** [*Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring*](https://arxiv.org/pdf/1802.04633) | Noor/Shashank | Joseph/Nils | MS1 Due