MIT-Harvard Communications Information Networks Circuits and Signals (CINCS) / Hamilton Institute Seminar


Date: March 10th, 2021

Time: 10 AM EST



Password: 995112


Title: “I need a better description”: An Investigation Into User Expectations For Differential Privacy

(joint work with Gabriel Kaptchuk and Elissa Redmiles)


Abstract: Despite recent widespread deployment of differential privacy, relatively little is known about what users think of differential privacy. In this work, we seek to explore users’ privacy expectations related to differential privacy. Specifically, we investigate (1) whether users care about the protections afforded by differential privacy, and (2) whether they are therefore more willing to share their data with differential private systems. Further, we attempt to understand (3) users’ privacy expectations of the differential private systems they may encounter in practice and (4) their willingness to share data in such systems. To answer these questions, we use a series of rigorously conducted surveys (n=2424).

We find that users care about the kinds of information leaks against which differential privacy protects and are more willing to share their private information when the risks of these leaks are less likely to happen.  Additionally, we find that the ways in which differential privacy is described in the wild haphazardly set users’ privacy expectations, which can be misleading depending on the deployment. We synthesize our results into a framework for understanding a user’s willingness to share information with differentially private systems, which takes into account the interaction between the user’s prior privacy concerns and how differential privacy is described to them.


Bio: Dr. Rachel Cummings is an Assistant Professor of Industrial Engineering and Operations Research at Columbia University. She was formerly an Assistant Professor of Industrial and Systems Engineering and Computer Science (by courtesy) at Georgia Tech. Her research interests lie primarily in data privacy, with connections to machine learning, algorithmic economics, optimization, statistics, and information theory. Her work has focused on problems such as strategic aspects of data generation, incentivizing truthful reporting of data, privacy-preserving algorithm design, impacts of privacy policy, and human decision-making. Dr. Cummings received her Ph.D. in Computing and Mathematical Sciences from the California Institute of Technology, her M.S. in Computer Science from Northwestern University, and her B.A. in Mathematics and Economics from the University of Southern California.  She is the recipient of an NSF CAREER award, Apple Privacy-Preserving Machine Learning Award, JP Morgan Chase Faculty Award, a Google Research Fellowship for the Simons Institute program on Data Privacy, a Mozilla Research Grant, the ACM SIGecom Doctoral Dissertation Honorable Mention, the Amori Doctoral Prize in Computing and Mathematical Sciences, a Caltech Leadership Award, a Simons Award for Graduate Students in Theoretical Computer Science, and the Best Paper Award at the 2014 International Symposium on Distributed Computing.   Dr. Cummings also serves on the ACM U.S. Public Policy Council’s Privacy Committee.