Faculté et Recherche
Differential privacy beyond algorithms: Challenges for successful deployment
03 avr
2025
11H20 - 12H35
Jouy-en-Josas
Anglais
Participer
ESD & ISOM Seminar Series
Intervenant: Rachel Cummings (Columbia)
Salle T-017
Abstract:
Differential privacy (DP) has been hailed as the gold standard of privacy-preserving data analysis, by providing strong privacy guarantees while still enabling use of potentially sensitive data. Formally, DP gives a mathematically rigorous worst-case bound on the maximum amount of information that can be learned about an individual's data from the output of a computation. In the past two decades, the privacy community has developed DP algorithms that satisfy this privacy guarantee and allow for accurate data analysis for a wide variety of computational problems and application domains. We have also begun to see a number of high-profile deployments of DP systems in practice, both at large technology companies and government entities. Despite the promise and success of DP thus far, there are a number of critical operations challenges left to be addressed before DP can be easily deployed in practice, including: mapping the mathematical privacy guarantees onto protection against real-world threats, developing explanations of its guarantees and tradeoffs for non-technical users, integration with other privacy & security tools, preventing misuse, and more.
Bio:
Dr. Rachel Cummings is an Associate Professor of Industrial Engineering and Operations Research and (by courtesy) Computer Science at Columbia University, where she is also a member of the Data Science Institute and co-chairs the Cybersecurity Research Center. Before joining Columbia, she was an Assistant Professor of Industrial and Systems Engineering and (by courtesy) Computer Science at the Georgia Institute of Technology, and she received her Ph.D. in Computing and Mathematical Sciences from the California Institute of Technology. Her research interests lie primarily in data privacy, with connections to machine learning, algorithmic economics, optimization, statistics, and public policy. Dr. Cummings is the recipient of numerous awards including an NSF CAREER award, a DARPA Young Faculty Award, a DARPA Director’s Fellowship, an Early Career Impact Award, multiple industry research awards, a Provost’s Teaching Award, two doctoral dissertation awards, and Best Paper Awards at DISC 2014, CCS 2021, and SaTML 2023. Dr. Cummings also serves or has served on the U.S. Census Scientific Advisory Committee, the ACM U.S. Technology Policy Committee, the IEEE Standards Association, and the Future of Privacy Forum’s Advisory Board, and was a Fellow at the Center for Democracy & Technology.