Introduction to Data Privacy
CS/SS 152
Fall 2014

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

Instructor

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

Teaching Assistants

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.

Description

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.

Prerequisites

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

Evaluation

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:

• 3 Homeworks (10% each)
• Regular in-class checkpoints (10% total)
• Assigned blog posts (10% total)
• Participation (e.g., speaking up in class, filling out evaluation forms for classmate presentations, participation in your reading and research group discussions, general participation on the course blog) (5%)
• Group-based teaching (25%)
• Project (5% for the proposal, 5% for the presentation, and 10% for the write-up)

Textbook

Resources from similar courses

• The Algorithmic Foundations of Data Privacy, taught by Aaron Roth at UPenn.
• Algorithmic Challenges in Data Privacy, taught by Sofya Raskhodnikova and Adam Smith at Penn State.
• Foundations of Privacy, taught by Moni Naor, at the Weizmann Institute.
• Mathematical Approaches to Data Privacy , taught by Salil Vadhan at Harvard.

• CS/EE 144: The Ideas Behind the Web