Class Time: Fridays 10:10-12:00pm
Class Location: 480 Computer Science Building (except for 01/24 when it will be held in 488 CSB)
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.
For each of these technologies, our educational goal is two-fold: we both provide students with an understanding of the theoretical underpinnings of that technology and with working knowledge of where and how that technology can be applied in practice, along with the challenges and tradeoffs that may arise from such deployments. To this end, we organize our discussion of each technology in two stages: first, instructors introduce and demonstrate the basic theoretical concepts in a lecture-style manner; then, we all read technical materals (such as scientific papers or documentation) related to the technology and discuss them in class. We bias our selection of reading materials toward those that discuss practical application of the technologies at large institutions, such as Google, Apple, Meta, and governmental agencies.
Assignments in this class are also organized based on our two-fold educational goal:
Finally, students will read and discuss a list of assigned papers, which help them understand the progress that’s been made for the privacy technologies we study.