CS 795: Topics in Privacy and Anonymity, Fall 2018

Course Overview

Course Description

This course will cover topics in 3 different areas of computer science research. The heaviest will be on differential privacy, which is a rigorous approach for making data available and useful, while still maintaining privacy for the individual data contributors. We will then look at anonymity, which the aim of understanding how users might interact with online data, without unwittingly sacrificing their own information. Finally, we will take a brief look at fairness in algorithms, to gain a deeper appreciation of why privacy is so important.

Students will learn how to carefully read and present technical papers. They will learn how to think rigorously about privacy, and will gain a technical understanding of the inherent tradeoff that is faced when providing and using data.


Course Requirements

The course is heavily focused on reading, discussing and presenting of research papers. There will be a reading assignment each week. Students are expected to summarize each reading assignment, to post some questions about each reading, and to answer each other's questions on the Piazza discussion board. Additionally, each student will be assigned to present twice during the semester. In the week that they are presenting, they will be expected to lead the discussion from the white board, presenting the details of technical claims and proofs.

Presentation: 50%
Paper summaries: 25%
Online discussion: 25%

Presentation Requirements Your responsibility as presenter is to help the class understand the main technical claims of the paper, and their proofs. On the one hand, this does not necessarily mean you have to cover every single Lemma along the way: it is OK to ask us to accept certain things without proof. On the other hand, this does mean you may need to step back and provide some background. It is up to you to determine what background material might be necessary to include in your presentation! It is quite likely you will want to do some related reading, and possibly present some prior work. I suggest the following plan for preparing your presentation, though it is not required.


Tentative Schedule

Date Topic Links Presenter
Aug. 30 Intro to differential privacy The Algorithmic Foundations of Differential Privacy Dov
Sep. 13 (Thurs) Intro to differential privacy The Algorithmic Foundations of Differential Privacy Dov
Sept. 17 Differential Privacy: Strong Composition The Algorithmic Foundations of Differential Privacy Phi Hung
Sept. 27 (Thurs.) Smooth sensitivity Nissim et al., 2007 Phi Hung and Xavier
Oct. 4 (Thurs.) Differentially oblivious algorithms Chan et al., 2017 Sahar
Oct. 9 (Tues.) Differential Privacy for ML: deep learning Abadi et al. Sahar and Juan
Oct. 15 Differential privacy for graphs Kasiviswanathan et al., 2013 Mingyu
Oct. 22 Anonymous routing: Atom Atom Jenny and Chuck
Oct. 29 Anonymous routing: Vuvuzela Vuvuzela Chuck
Nov. 5 Anonymous routing: Stadium Stadium Mingyu
Nov. 12 Fairness: overview Berk et al., 2017 Jenny
Nov. 19 Fairness Through Awareness Dwork et al. Juan
Nov. 26 Fairness: Inherent trade-offs Kleinberg et al.~,2016 Xavier