Academics / Courses / DescriptionsCOMP_SCI 312, 412: Data Privacy
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Prerequisites
(COMP_SCI 211, 212 and 214) or Instructor consent (Programming experience and familiarity with basics of discrete math and statistics/probability)Description
Data breaches, privacy breaches, and concerns about algorithmic decision-making have been on the rise. As a result, data privacy has become an increasingly significant concern in the past several years. Individuals and organizations often trust institutions with their data with the expectation that one's data is private from others or to the handling institutions and that it is not used for unfair practices. To ensure the privacy of sensitive data, privacy mechanisms have been developed to preserve the privacy of data without reducing its functionality.
The goal of this course is to introduce students to the concept and implications of data privacy, including mechanisms and protocols that are used to preserve data privacy in practice. We will study concepts such as differential privacy, database anonymization, anonymous communication, and algorithmic fairness. We will also discuss privacy in the context of web privacy, social network privacy, human factors, and machine learning along with any policy implications.
- This course fulfills the Project area.
- Formerly Comp_Sci 397/497 - last offer was Winter 2023
REFERENCE TEXTBOOKS: N/A
REQUIRED TEXTBOOK: There is no assigned textbook for this course. Course materials will include slides, online resources, and assigned readings.
COURSE COORDINATORS: Prof. Sruti Bhagavatula
COURSE INSTRUCTOR : Prof. Sruti Bhagavatula