Instructor: Michael Dinitz
Lecture: T Th 9am - 10:15am, Hodson 311
Office Hours: By appointment
Teaching Assistant: Shruthi Prusty

Resources

• Syllabus
• Textbook: The Algorithmic Foundations of Differential Privacy, by Cynthia Dwork and Aaron Roth
• Online discussion board: Courselore. To join, use this invite link

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Schedule

Date	Topic	Reference	Notes
Jan 21	Introduction, Definitions	Chapters 1, 2

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Assignments

Please submit all assignments using Gradescope

Final Project Information

This class will have a final project, whose exact form is flexible and mostly up to you, subject to approval by the instructor. You can work in small groups if you want. Possible ideas include:

• Doing new research in algorithmic aspects of differential privacy.
• Doing research on a problem in your research area that can be combined with differential privacy (e.g., machine learning with privacy, HCI with privacy, etc.)
• Preparing a lecture / presentation / writeup on some aspect of differential privacy that we did not cover in class.

Other Resources

Other textbooks:

• The Complexity of Differential Privacy, by Salil Vadhan

Other classes:

• Algorithmic Foundations of Data Privacy, Penn
• Algorithms for Private Data Analysis, Waterloo