We will study privacy in a few settings where rigorous definitions and enforcement mechanisms are being developed–statistical disclosure limitation, semantics and logical specification of privacy policies that constrain information flow and use, principled audit and accountability mechanisms for enforcing privacy policies, anonymous communication protocols.

Syllabus

http://course.ece.cmu.edu/~ece734/

Class format

Lecture and project-based

Home departments

Software and Societal Systems, ECE

Target audience

Software and Societal Systems and ECE MS/Ph.D. students

Background required

An undergraduate course equivalent to 15-251 or permission of instructor.

Learning objectives

• Understand formal, quantitative techniques for measuring and evaluating the privacy of algorithms and computer systems
• Learn about different techniques for provably protecting the privacy of a dataset
• Learn how algorithms can exhibit bias against populations and individuals
• Understand common countermeasures for training fair algorithms, and when to use which countermeasure

Faculty and instructors who have taught this course in the past

Anupam Datta, Giulia Fanti, Steven Wu