Introduction to Data Privacy.
9 units (3-0-6); first term.
Prerequisites: Ma 3, CS 24 and CS 38, or instructor's permission.
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. Not offered 2016-17.