Privacy in the World of Big Data, Spring 2016
Spring 2017 webpage
Time: Mondays, 2pm-5:20pm.
Location: VKC 108.
Instructor: Aleksandra Korolova. (Office hours: Wednesdays 4:30pm-5:30pm, and by appointment in SAL 206).
TA: Brendan Avent. (Office hours: Thursdays 1:30pm-2:30pm, and by appointment in SAL 246).
Assignments: See schedule.
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 publishing search logs and location traces, building recommender systems and telemetry for malware detection).
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, advertising, anonymity and surveillance, and algorithmic fairness.
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 with sufficient mathematical maturity are welcome.