Privacy in the World of Big Data, Spring 2021

Time: Mondays, 2pm-5:20pm.

See Schedule for class material and assignments.

Location: Online.

Syllabus: tentative syllabus.

Instructor: Aleksandra Korolova. (Office hours: TBA).


A graduate level introduction to the privacy challenges that arise as a result of ubiquitous use of technology, dropping data collection, storage, and analysis costs, and data-based technological innovation, as well as algorithmic and technological approaches to addressing them.

The first half of the course will focus on statistical data privacy – the problem of making useful inferences based on data of many individuals while ensuring that each individual’s privacy is preserved. We will survey plausible-sounding approaches that fail to achieve this goal, followed by a study of privacy definitions and algorithms for achieving both privacy and utility (including in real-world applications such as data publishing by US Census).

The second half of the course will survey the technical aspects of topics and technologies at the frontier of current privacy-related discourse, such as web (and other forms of) tracking and its unintended consequences and new models for data access such as federated learning

Our aim is to explore cutting-edge research topics in privacy, with a balance between theory and practical applications. The final syllabus and list of topics will be tailored to the backgrounds and interests of enrolled students.

The course is geared toward Ph.D. students who want to gain familiarity with privacy from a scientific perspective. Advanced undergraduates and MS students with sufficient mathematical maturity are welcome.

Prerequisites: solid grasp of advanced algorithms, proof-based mathematics, and probability.