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Algorithms for Private Data Analysis


This course studies the following question in this course:

How do we perform useful analysis on a data set that contains sensitive information about individuals without compromising the privacy of those individuals?

To study this question, we will introduce differential privacy, a framework of designing data analysis algorithms with strong, meaningful, and mathematically provable privacy guarantees. We will survey a set of algorithmic tools that allow us to privately perform a wide range of statistical analyses and machine learning tasks. Of course, privacy does not come for free, and we will also study some of the fundamental limitations imposed by the requirement of differential privacy. We will also cover some novel and surprising connections between differential privacy and and machine learning, game theory, and cryptography.


  • Course: 17880 Algorithms for Private Data Analysis, Spring 2021
  • Zoom Link: See Canvas
  • Time: Spring 2021, 10:40AM -- 12:00PM, Mon & Weds


  • Steven Wu
    • Email:
    • Office hours: Friday 9:00pm - 10:00am ET
    • Location: See Canvas or email the instructor to schedule a time


This is a theory-oriented course, intended for graduate students and advanced undergraduates. The (informal) prerequisites are mathematical maturity, ability to read and engage with original research, and familiarity with probability and introductory algorithms. Prior coursework in machine learning, algorithms, and probability will be helpful.


There is no need buy any textbook for this course. We will provide lecture notes in this course. In addition, we will frequently use the book The Algorithmic Foundations of Differential Privacy by Cynthia Dwork and Aaron Roth and The Complexity of Differential Privacy by Salil Vadhan.

Diversity Statement

It is our goal that students from all diverse backgrounds and perspectives are well served by this course, that students’ learning needs be addressed both in and out of class, and that the diversity that students bring to this class be viewed as a resource, strength, and benefit. Dimensions of diversity include race, age, national origin, ethnicity, gender identity and expression, intellectual and physical ability, sexual orientation, faith and non-faith perspectives, socio-economic class, political ideology, education, primary language, family status, military experience, cognitive style, and communication style. We are intentional in our aim to present materials and activities that are respectful of diversity, based on these dimensions and any other visible and invisible differences not captured in this list. Indeed, in this class you will learn to approach technology design from an empathetic, human-centered perspective that directly examines and challenges bias and inequality. Your suggestions for ensuring that the class lives up to these values are encouraged and welcomed. In addition, if at any time you experience or witness anything in this class that challenges inclusion, is insensitive or othering, or reinforces biases or stereotypes, please report those experiences (responses can be anonymous).


  • Canvas: We will be using Canvas for all assignments and grades. Please also post all questions on Canvas as discussions instead of sending emails.

  • Email: If you email your instructors, you might want to include the substring "DP Course" to begin a meaningful subject line and have tried to resolve the issue appropriately otherwise. For example, you should post questions about course material and homework assignments on Canvas first, and then use emails only after an appropriate amount of time has passed without a response. Please use your CMU email account.


Percentage Activity
20% In-class Activity Participation
50% Assignment 1-4, 12.5% each
30% Final Project

When Work is Due

  • Projects and assignments are due on Sundays at 11:59pm.

Late Assignments

We realize that things happen, and that you might sometimes not be able to turn in your assignments. To accommodate this, you will each receive 5 free late days.

Beyond those days, you receive a 5% penalty for each day late. You are welcome to budget late days as you like, for instance 3 free days late for one assignment, 2 for another, or all 5 for one.

Special Accommodations

If you have a disability and have an accommodation letter from the Disability Resources office, we encourage you to discuss your accommodations and needs with us as early in the semester as possible. We will work with you to ensure that accommodations are provided as appropriate. If you suspect that you may have a disability and would benefit from accommodations but are not yet registered with the Office of Disability Resources, we encourage you to contact them at

Health & Wellness

Take care of yourself. Do your best to maintain a healthy lifestyle this semester by eating well, exercising, avoiding drugs and alcohol, getting enough sleep and taking some time to relax. This will help you achieve your goals and cope with stress.

All of us benefit from support during times of struggle. There are many helpful resources available on campus and an important part of the college experience is learning how to ask for help. Asking for support sooner rather than later is almost always helpful.

If you or anyone you know experiences any academic stress, difficult life events, or feelings like anxiety or depression, we strongly encourage you to seek support. Counseling and Psychological Services (CaPS) is here to help: call 412-268-2922 and visit their website at Consider reaching out to a friend, faculty or family member you trust for help getting connected to the support that can help.

If you or someone you know is feeling suicidal or in danger of self-harm, call someone immediately, day or night:

CaPS: 412-268-2922

Re:solve Crisis Network: 888-796-8226