NCSA staff who would like to submit an item for the calendar can email newsdesk@ncsa.illinois.edu.
Differential privacy (DP) is widely regarded as a gold standard for privacy-preserving computation over users’ data. A key challenge is that the privacy guarantees are difficult to communicate to users, leaving them uncertain about how and whether they are protected. Despite recent widespread deployment of DP, relatively little is known about user perceptions and how to effectively communicate DP’s practical privacy guarantees. This talk will cover a series of user studies aimed at measuring and improving communication with non-technical end users about DP. The first set explores users’ privacy expectations related to DP and measures the efficacy of existing methods for communicating the privacy guarantees of DP systems. We find that the ways in which DP is described in-the-wild largely set users’ privacy expectations haphazardly, which can be misleading depending on the deployment. Motivated by these findings, the second set develops and evaluates prototype descriptions designed to help end users understand DP guarantees. These descriptions target two important technical details in DP deployments that are often poorly communicated to end users: the privacy parameter epsilon (which governs the level of privacy protections) and distinctions between the local and central models of DP (which governs who can access exact user data).
Rachel Cummings is an assistant professor of Industrial Engineering and Operations Research at Columbia University, an Affiliate in the Department of Computer Science (by courtesy), and a member of the Data Science Institute. Her primary research interest is data privacy, with connections to machine learning, algorithmic economics, optimization, statistics, and public policy. 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, a DARPA Young Faculty Award, an Apple Privacy-Preserving Machine Learning Award, a JP Morgan Chase Faculty Award, a Google Research Fellowship, a Mozilla Research Grant, the ACM SIGecom Doctoral Dissertation Honorable Mention, the Amori Doctoral Prize in Computing and Mathematical Sciences, a Simons Award for Graduate Students in Theoretical Computer Science, and Best Paper Awards at CCS (2021) and DISC (2014). She serves on the ACM U.S. Public Policy Council’s Privacy Committee and the Future of Privacy Forum’s Advisory Board.