Data Privacy

CS 475 looks at privacy as a technical property you can define, measure, and break — not just a policy concern — and surveys the tools (anonymization, cryptographic protocols, differential-privacy-flavored ML defenses) that try to preserve it across domains like genomics, location traces, blockchains, and e-voting. The work is reading-heavy: weekly lectures pair with a term project and student presentations on recent papers, plus quizzes and a final to keep you honest on the foundations. It sits as a senior elective that assumes you're comfortable with algorithms and basic crypto, and it's the natural follow-up if security courses left you wondering how systems actually leak information even when nothing is obviously "hacked."