Special Topics in Data Privacy
CS101C, Section 01
Spring 2013

Tuesday/Thursday 10:30 - 11:55
213 Annenberg (may change due to enrollment)


Katrina Ligett, 316 Annenberg. Office hours Mondays 10:30 - noon. Office hours may move to Annenberg 308, as needed.

Teaching Assistants

Akshay Pai
Nilanjan Roy
Initial office hours will be announced on Piazza.


Please sign up for the course page on Piazza and monitor it throughout the term. We will be using Piazza to post assignments and updates, and to conduct class discussions. Please post your questions there (you can also post only for course staff to see).


How should we define privacy? What are the tradeoffs between useful computation on large datasets and the privacy of those from whom the data is derived? This course will take a mathematically rigorous approach to addressing these and other questions at the frontier of research in data privacy. We will draw connections with a wide variety of topics, including economics, statistics, information theory, game theory, probability, learning theory, geometry, and approximation algorithms.


No formal prerequisites. This course is intended for graduate students and advanced undergraduates. It is expected that students have the technical maturity to read and engage with original research and are comfortable with probability and introductory algorithms. Ma 2b, CS 24 and CS 38 may be quite useful.


Your grade in the course will be based on a mix of work completed individually and that completed in cooperation with your reading and research group. This is a preliminary breakdown that may change during the term, particularly as enrollment levels settle:

Resources from similar courses

This course's design, content, and website are based in part on similar courses: and on one less-related course: