Protecting Individual Privacy using Differential Privacy

Data scientists in a number of fields including medicine, internet of things, and social science, routinely gather and analyze individual-level data. These data span every aspect of our lives and, thus, could breach our privacy by revealing medical diagnoses, sexual orientation, race and other sensitive properties about us. Differential privacy is a principled approach for data analysis with provable guarantees of privacy for individuals.

In this summer project, student teams built usable interfaces to sensitive datasets through which users can query and analyze the sensitive data (medical and location trajectories) while satisfying differential privacy using state of the art differentially private algorithms.

Participating Students

Yikai Wu

Yikai Wu
I am a rising junior from China, majoring in computer science and physics. I have been working with Prof. Machanavajjhala throughout the year on differential privacy. I find it an interesting topic as it involves database analysis and algorithmic design with a variety of mathematical techniques. In the future, I would like to pursue a PhD in computer science and hopefully become a professor or a researcher.

Justina Zou

Justina Zou
I'm majoring in Statistical Science, and I'm interested in my summer project because I want to learn more about how to protect privacy using computer science and statistics. In the future, I hope to learn more about the intersection of statistics, math, and computer science and how they can be used to solve challenging problems.

Thomas Butler

Thomas Butler
I am a first year CS major from Richmond, VA. I am interested in this summer project because it gives us an opportunity to provide researchers at Duke with important health data while allowing the subject individuals to maintain their privacy. While our health data can help researchers make important breakthroughs, it is equally, if not more, important that subjects can stay anonymous. My hope is to graduate with a concentration in Software Systems and continue to work with researchers and data firms to provide users privacy in an age where so much is in the public domain.

Yunyao Zhu

Yunyao Zhu
A native of Shanghai, China, Yunyao is a rising sophomore majoring in Computer Science. Interested in the intersection of ethics and technology, she is excited to learn more about the techniques that protect individual privacy. She hopes to continue learning and applying technology for social good both at Duke and beyond graduation.


Our undergraduate summer program concluded August 2, 2019 with a research showcase and projects fair which featured posters demonstrating what our students have been doing all summer.

Protecting Individual Privacy using Differential Privacy-Summer Research Project Poster 2019-Picture1
Summer Research Showcase: Privacy Project, with participating students.

Protecting Individual Privacy using Differential Privacy-Summer Research Project Poster 2019-Picture2
Summer Research Showcase: Privacy Project, with participating student and Pankaj Agarwal, Chair and Professor.

Protecting Individual Privacy using Differential Privacy-Summer Research Project Poster 2019-Picture3
Summer Research Showcase: Privacy Project, with participating students and Professor Jeff Forbes.


On July 19 at the end of week 7 of their 10-week summer program developing research projects with computer science faculty, students presented their results to date. The audience was comprised of about 100 people, including students and mentors from Data+, Code+, and SUPICS, the summer undergraduate projects in computer science. Below, Yikai Wu presents with his teammates (from left to right: Justina Zou, Thomas Butler, and Yunyao Zhu) on their summer project studying differential privacy in data analysis.

Privacy July 19 Presentations