Adjusting paper presentation format.

Plan: fix topic for each lecture and two suggested papers each.

website/docs/org.md

@@ -25,49 +25,47 @@ These three components are detailed below.
 
 ### Paper presentations
 
-**Paper discussions** are one of the main components of this course. Before
+**Paper discussions** are one of the main components of this course. In groups
-every presentation, you are expected to read the paper closely and understand
+of two (or very rarely three), you will present 2-3 papers on a related topic
-its significance, including (a) the main problem addressed by the paper, (b) the
+and lead the discussion; we will have presentations most Wednesdays and Fridays.
-primary contributions of the paper, and (c) how the authors solve the problem in
+Your presentation should last about **60 minutes** long, leaving the remainder
-some technical detail. Of course, you are also expected to attend discussions
+of the time for a wrap-up discussion. Please sign up for a slot and a paper by
-and actively participate in the discussion.
+**Monday, September 9**; while we will try to accommodate everyone's interests,
+we may need to adjust the selections for better balance and coverage.
 
-The topics we will be reading about are from the recent research
+Before every presentation, all students are expected to read the papers closely
-literature---peer-reviewed and published, but not completely refined.  Most
+and understand their significance, including (a) the main problems, (b) the
+primary contributions, and (c) how the technical solution. Of course, you are
+also expected to attend discussions and actively participate in the discussion.
+
+We will be reading about topics from the recent research literature. Most
 research papers focus on a very narrow topic and are written for a very specific
 technical audience. It also doesn't help that researchers are generally not the
 clearest writers, though there are certainly exceptions. These
 [notes](https://web.stanford.edu/class/ee384m/Handouts/HowtoReadPaper.pdf) by
 Srinivasan Keshav may help you get more out of reading papers.
 
-To help you prepare for the class discussions, I will also send out a few
-questions at least 24 hours before every paper presentation. **Before** each
-lecture, you should send me brief answers---a short email is fine, no more than
-a few sentences per question. These questions will help you check that you have
-understood the papers---they are not meant to be very difficult or
-time-consuming and they will not be graded in detail.
-
 ### Homeworks
 
 There will be three small homework assignments, one for each of the core
-modules. You will play with software implementations of the methods we cover in
+modules, where you will play with software implementations of the methods we
-class. These assignments are not weighted heavily, though they will be lightly
+cover in class. These assignments will be lightly graded; the goal is to give
-graded; the goal is to give you a chance to write some code.
+you a chance to write some code and run some experiments.
 
 ### Course Project
 
-The main component is the **course project**. You will work individually or in
+The main course component is the **course project**. You will work individually
-pairs on a topic of your choice, producing a conference-style write-up and
+or in pairs on a topic of your choice, producing a conference-style write-up and
-presenting the project at the end of the semester. Successful projects may have
+presenting the project at the end of the semester. The best projects may
-the potential to turn into an eventual research paper or survey. Details can be
+eventually lead to a research paper or survey. Details can be found
-found [here](assignments/project.md).
+[here](assignments/project.md).
 
 ## Learning Outcomes
 
 By the end of this course, you should be able to...
 
 - Summarize the basic concepts in differential privacy, applied cryptography,
-  language-based security, and adversarial machine learning.
+  and adversarial machine learning.
 - Use techniques from differential privacy to design privacy-preserving data
   analyses.
 - Grasp the high-level concepts from research literature on the main course

website/docs/resources/readings.md

@@ -24,6 +24,13 @@
   ASIACRYPT 2015.
 - Matthew Joseph, Aaron Roth, Jonathan Ullman, and Bo Waggoner.
   [*Local Differential Privacy for Evolving Data*](https://arxiv.org/abs/1802.07128).
+  NIPS 2018.
+- Albert Cheu, Adam Smith, Jonathan Ullman, David Zeber, and Maxim Zhilyaev.
+  [*Distributed Differential Privacy via Shuffling*](https://arxiv.org/pdf/1808.01394).
+  EUROCRYPT 2019.
+- Jingcheng Liu and Kunal Talwar.
+  [*Private Selection from Private Candidates*](https://arxiv.org/pdf/1811.07971).
+  STOC 2019.
 
 ### Adversarial Machine Learning
 - Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus.
@@ -47,6 +54,12 @@
 - Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu.
   [*Towards Deep Learning Models Resistant to Adversarial Attacks*](https://arxiv.org/pdf/1706.06083.pdf).
   ICLR 2018.
+- Vitaly Feldman.
+  [*Does Learning Require Memorization? A Short Tale about a Long Tail*](https://arxiv.org/pdf/1906.05271).
+  arXiv 2019.
+- Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song.
+  [*The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks*](https://arxiv.org/pdf/1802.08232).
+  USENIX Security 2019.
 
 ### Applied Cryptography
 - Benjamin Braun, Ariel J. Feldman, Zuocheng Ren, Srinath Setty, Andrew J.  Blumberg, and Michael Walfish.
@@ -73,6 +86,15 @@
 - Arjun Narayan, Ariel Feldman, Antonis Papadimitriou, and Andreas Haeberlen.
   [*Verifiable Differential Privacy*](https://www.cis.upenn.edu/~ahae/papers/verdp-eurosys2015.pdf).
   EUROSYS 2015.
+- Henry Corrigan-Gibbs and Dan Boneh.
+  [*Prio: Private, Robust, and Scalable Computation of Aggregate Statistics*](https://people.csail.mit.edu/henrycg/files/academic/papers/nsdi17prio.pdf).
+  NSDI 2017.
+- Valerie Chen, Valerio Pastro, Mariana Raykova.
+  [*Secure Computation for Machine Learning With SPDZ*](https://arxiv.org/pdf/1901.00329).
+  NIPS 2018.
+- Wenting Zheng, Raluca Ada Popa, Joseph E. Gonzalez, Ion Stoica.
+  [*Helen: Maliciously Secure Coopetitive Learning for Linear Models*](https://arxiv.org/pdf/1907.07212).
+  S&P 2019.
 
 ### Algorithmic Fairness
 - Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Rich Zemel.

website/docs/schedule/lectures.md

@@ -7,34 +7,34 @@
 9/6   | Basic private mechanisms <br> **Reading:** AFDP 3.2-4 |
 9/9   | Composition and closure properties <br> **Reading:** AFDP 3.5 | Signups
 9/11  | What does differential privacy actually mean? <br> **Reading:** McSherry. [Lunchtime for Differential Privacy](https://github.com/frankmcsherry/blog/blob/master/posts/2016-08-16.md) |
-9/13  | Paper presentations | HW1 Due
+9/13  | Paper presentations: Differential privacy | HW1 Due
       | <center> <h4> **Adversarial Machine Learning** </h4> </center> |
-9/16  | Overview and Basic attacks | HW2 Out
+9/16  | Overview and basic concepts | HW2 Out
-9/18  | More attacks |
+9/18  | Paper presentations: Adversarial attacks |
-9/20  | Paper presentations |
+9/20  | Paper presentations: ??? |
-9/23  | Defense: Adversarial training |
+9/23  | Adversarial training |
-9/25  | Defense: Certified defenses |
+9/25  | Paper presentations: Certified defenses |
-9/27  | Paper presentations | HW2 Due
+9/27  | Paper presentations: ??? | HW2 Due
       | <center> <h4> **Applied Cryptography** </h4> </center> |
 9/30  | Overview and basic constructions | HW3 Out
-10/2  | Secure Multiparty Computation |
+10/2  | Paper presentations: Secure Multiparty Computation |
-10/4  | Paper presentations |
+10/4  | Paper presentations: ??? |
 10/7  | Homomorphic Encryption |
-10/9  | Oblivious computing and side channels |
+10/9  | Paper presentations: Oblivious computing and side channels |
-10/11 | Paper presentations | HW3 Due <br> MS1 Due
+10/11 | Paper presentations: ??? | HW3 Due <br> MS1 Due
       | <center> <h4> **Advanced Topic: Algorithmic Fairness** </h4> </center> |
 10/14 | Overview and basic notions |
-10/16 | Individual and group fairness |
+10/16 | Paper presentations: Individual and group fairness |
-10/18 | Paper presentations |
+10/18 | Paper presentations: ??? |
-10/21 | Repairing fairness |
+10/21 | Challenges in defining fairness |
-10/23 | Challenges in defining fairness |
+10/23 | Paper presentations: Repairing fairness |
-10/25 | Paper presentations |
+10/25 | Paper presentations: ??? |
       | <center> <h4> **Advanced Topic: PL and Verification** </h4> </center> |
 10/28 | Overview and basic notions |
-10/30 | Programming languages for differential privacy |
+10/30 | Paper presentations: Probabilistic programming languages |
-11/1  | Paper presentations |
+11/1  | Paper presentations: ??? |
-11/4  | Probabilistic programming languages |
+11/4  | Programming languages for differential privacy |
-11/6  | Verifying probabilistic programs |
+11/6  | Paper presentations: Verifying probabilistic programs |
-11/8  | Paper presentations | MS2 Due
+11/8  | Paper presentations: ??? | MS2 Due
       | <center> <h4> **No Lectures: Work on Projects** </h4> </center> |
 12/11 (TBD) | Project Presentations |