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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Atlanta, GA | Posted: April 2, 2020
H. Milton Stewart School of Industrial and Systems Engineering
Summary Sentence:
Cummings’ project is titled “Algorithms, Incentives, and Policy for Data Privacy,” under the NSF Secure and Trustworthy Cyberspace program
Full Summary:
Cummings’ project is titled “Algorithms, Incentives, and Policy for Data Privacy,” under the NSF Secure and Trustworthy Cyberspace program.
ISyE Assistant Professor Rachel CummingsRachel Cummings, an assistant professor in the H. Milton Stewart School of Industrial and Systems Engineering (ISyE), has been awarded a CAREER grant from the National Science Foundation (NSF). The CAREER grant is NSF’s most prestigious award in support of early-career faculty who have the potential to serve as academic role models in research and education and to lead advances in the mission of their department or organization.
The $488,884 award runs through February 28, 2025. Cummings’ project is titled “Algorithms, Incentives, and Policy for Data Privacy,” under the NSF Secure and Trustworthy Cyberspace program. She will be examining how privacy concerns impact data usage and how to address new technical challenges that arise when theoretical privacy technologies are implemented in real-world settings.
Cummings studies a parametrized privacy notion known as differential privacy. It provides a mathematically rigorous bound on the amount of information leaked about an individual by performing an analysis on a dataset that contains her information. Differential privacy has quickly become viewed as the gold standard for privacy-preserving data analysis, and has been deployed by major organizations such as Microsoft, Google, Apple, Uber, and the U.S. Census Bureau.
These real-world implementations bring about new technical challenges for differential privacy, which is the focus of this award. These challenges include optimally spreading a privacy budget across multiple analysis tasks, designing privacy policies that match users’ context-dependent expectations, and regulating markets for personal information to balance privacy, fairness, and economic value. This award also includes a significant educational and outreach component, including workshop organization aimed at improving diversity in graduate education.
Cummings joined ISyE in 2017. She received her B.A. in mathematics and economics from the University of Southern California (2011), her M.S. in computer science from Northwestern University (2013), and her Ph.D. in computing and mathematical sciences from the California Institute of Technology (2017).
She is the recipient of a Google Research Fellowship, a Simons-Berkeley Research Fellowship in Data Privacy, 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.
Cummings also serves on the ACM U.S. Public Policy Council's Privacy Committee.
