Class Time: Thursdays 10:10am-12pm
Class Location: CSB 480
Data scientists and engineers have significant ethical and legal responsibilities to protect the privacy and best interests of the customers whose data they collect and use. This class discusses these responsibilities, the challenges of meeting them in practice, and a set of advanced technologies that can be used to enhance privacy, accountability, and data protection in big data systems. The focus (and uniqueness) of the class is to look at these technologies with a systems perspective of incorporating them in real data infrastructure systems. The class will cover advanced privacy technologies such as differential privacy, homomorphic databases, secure multi-party computation, hardware enclaves, and private federated learning.
The class is organized in two parts, each taking roughly half of the semester:
PART 1: Basic Concepts is lecture-based and reviews the basic concepts and theory behind privacy technologies. To exercise these concepts, students will complete three well-structured, individual homeworks and will take a midterm.
PART 2: Applied Technologies is paper- and discussion-based and surveys applications of the PART 1 basic concepts in real deployments and systems. We survey deployments from Google, Apple, Microsoft, and governmental agencies. To develop their own applications of privacy technologies, the students will work on a half-semester team project on applied privacy science.