- Data Privacy

Catalog description

The explosion of data collection and advances in artificial intelligence and machine learning have motivated a robust economy around data-based services. While such services provide opportunities for a broad array of individuals and companies to leverage the power of modern data analytics and machine learning, this new economy also exposes new vulnerabilities and privacy risks. This course will explore the growing area of data privacy in modern computing systems including formal frameworks such as differential privacy and secure multiparty computation. Students will work to understand techniques, issues, and trade-offs related to data privacy in a computing context. In particular, students will study: definitions of data privacy, techniques for achieving privacy, limitations and trade-offs inherent in various settings, and the relationship between privacy policy and privacy technology. The department strongly recommends that students enrolling in this course have earned a grade of C or higher in Data Structures ( CS 271 ). This course is classified as a theory elective.