263-4658-00L Privacy Enhancing Technologies

Abstract	Privacy is a fundamental human right! And yet, technological advances (in particular in computer science) can often undermine privacy. In this class we will see how to formalize various notions of privacy and how to build systems that preserve privacy, by combining techniques from cryptography and statistics. The later parts of the course will focus on applications to machine learning.
Learning objective	By the end of the course, students will be able to: - Reason about privacy concerns and the appropriate formalizations - Combine tools from cryptography and statistics to build privacy mechanisms - Assess, evaluate and prove privacy protection of a mechanism.
Content	The first half of the class will cover topics from cryptography such as secure multiparty computation, zero-knowledge proofs, PIR, ORAM, anonymous communication, etc. The second half will cover statistical notions of privacy, in particular differential privacy, and selected topics in machine learning privacy.
Lecture notes	Lecture notes will be posted on Moodle.
Literature	Boneh & Shoup - A Graduate Course in Applied Cryptography References to relevant research papers will be provided
Prerequisites / Notice	Basic knowledge in cryptography, probability and machine learning is recommended but not required.