Privacy in Statistics and Machine Learning

Adam Smith
Jonathan Ullman

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Spring 2021
Privacy in Statistics and Machine Learning

Spring 2021

Course Overview: How can we learn from a data set of sensitive information while providing meaningful privacy to the individuals whose information it contains? The course explores this question, starting from the problems faced by straightforward solutions and moving on to rigorous state-of-the-art solutions using differential privacy. The class will focus on foundations, but also delve into some applied work and on some of the social, ethical, and legal context for the subject. Students will be required to complete some mathematical assignments, some light programming assignments, and a final course project.

These two MinutePhysics videos ( 1, 2) give a light introduction to the course topics in the context of the US decennial census.

Instructor Contact Office Hours on
Adam Smith Tue 11am-noon and Thu 3:15-4:15pm
Jonathan Ullman Tue and Fri 3:15-4:15pm


Teaching in Parallel: This course is being taught in parallel at BU and NEU. Administratively the courses are completely separate, but Jon and Adam will be preparing all of the course materials together, and we may cancel or reschedule one or two lectures to accommodate the differences in the academic calendar between the two institutions.

Time and Place:

For NEU students: note that we are only using 75 out of the scheduled 100 minutes. This is both to accommodate students at BU, which uses a different course schedule, and because the flipped classroom format will require you to do a bit more work outside of class, and we want you to get some of that time back.

Flipped Classroom: The instructors will be recording lecture videos, which will be made available via the course website. Scheduled class time will be used for discussion of the lecture material and collaboratively solving related problems. You are expected to watch the recorded lectures, come to scheduled class times, and participate actively in the discussion.

Course Topics: The exact set of topics will evolve as the course proceeds, but a representative list includes:

Please see the schedule tab for the most up-to-date information about the course topics.

Textbook: There is no official textbook for the class. Some good (and free) resources for the material are

Prerequisites: Students should have a solid grounding in probability and statistics, linear algebra, vector calculus, and algorithms. Students should be comfortable reading and writing mathematical proofs involving algorithms and probability.

Protocols for virtual classes: Remote lectures will take place using Zoom or similar software. While we understand that people are dealing with a variety of challenges, we ask you that you attend lectures synchronously and turn your camera on, and participate actively. If this causes hardship, either in general or in a given week, please contact us about it and we will work to find an accommodation.

Coursework and Grading: The grade will be based on:

Information about the assignments and final project can be found on the assignments tab. You are expected to watch the recorded lectures, come to scheduled class times, and participate actively in the discussion. Late work will not be accepted, except by agreement with the instructor. Make a request at least 48 hours ahead of the due date.

Auditors are welcome: In particular, students from other universities are welcome to attend and participate in discussions. If you're interested in auditing, please contact the instructors to introduce yourself!

Collaboration and academic conduct: You may discuss homework assignments and projects with classmates, but you are solely responsible for what you turn in. Collaboration in the form of discussion is allowed, but all forms of cheating (copying parts of a classmate’s assignment, plagiarism from papers or old posted solutions) are NOT allowed. A rough rule of thumb: you should be able to walk away from a discussion of a homework problem with no notes at all and write your solution on your own.

Late-work policy: In order to help you deal with unexpected problems or bursty work deadlines, we are giving everyone a budget of six late days to distribute as they see fit, no questions asked. You may use these late days on any assignment, including project milestones, except for the final project report. You may only use integer numbers of late days. For example, turning in an assignment 25 hours late counts for two late days. Additional extensions beyond your allocated late days be granted only in rare circumstances.

Course atmosphere, diversity and inclusion: We intend to provide a positive and inclusive atmosphere in classes (in-person or remote) and on the associated virtual platforms. Students from a wide range of backgrounds and with a diverse set of perspectives are welcome. We ask that students treat each other with thoughtfulness and respect, and do their part to make all their peers feel welcome. Your suggestions are encouraged and appreciated. Please let us know ways to improve the effectiveness of the course for you personally or for other students or student groups.