Introduction to Data Privacy
CS/SS 152
Fall 2014

Monday/Wednesday 1 - 2:25pm
213 Annenberg (may change due to enrollment)


Katrina Ligett, 316 Annenberg. Office hours Thursdays 10:30 - 11:30am.

Teaching Assistants

  • Juba Ziani. Office hours: Thursdays 2-3pm; 2-4pm when a homework is due. Annenberg 329, possibly Ligett studio (Annenberg 308) depending on attendance.
  • Rachel Cummings (volunteer TA). Office hours: Tuesday 1-2pm, Ligett studio (Annenberg 308).
  • Piazza

    Please sign up for the course page on Piazza and monitor it throughout the term. We will be using Piazza to post announcements, assignments and updates, and to conduct class discussions on project proposals and a broad range of privacy topics. Please post your questions there.


    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.


    Ma 3, CS 24 and CS 38, or instructor's permission.


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


    "The Algorithmic Foundations of Differential Privacy" by Cynthia Dwork and Aaron Roth.
    The pdf version is available for free; the paper version is on Amazon for $99.

    Resources from similar courses

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