Google TechTalks | Academic Keynote: Mean Estimation with User-level Privacy under Data Heterogeneity, Rachel Cummings @GoogleTechTalks | Uploaded February 2022 | Updated October 2024, 1 week ago.
A Google TechTalk, presented by Rachel Cummings, 2021/11/8
ABSTRACT: Mean Estimation with User-level Privacy under Data Heterogeneity (joint work with Vitaly Feldman, Audra McMillan, and Kunal Talwar).
A key challenge for data analysis in the federated setting is that user data is heterogeneous, i.e., it cannot be assumed to be sampled from the same distribution. Further, in practice, different users may possess vastly different number of samples. In this work we propose a simple model of heterogeneous user data that differs in both distribution and quantity of data, and we provide a method for estimating the population-level mean while preserving user-level differential privacy. We demonstrate asymptotic optimality of our estimator within a natural class of private estimators and also prove general lower bounds on the error achievable in our problem. We will conclude with a discussion of future challenges and possible extensions for learning from heterogeneous populations in the federated setting.
About the Speaker: Rachel Cummings, Columbia University
Dr. Rachel Cummings is an Assistant Professor of Industrial Engineering and Operations Research at Columbia University. Before joining Columbia, she was an Assistant Professor of Industrial and Systems Engineering and (by courtesy) Computer Science at the Georgia Institute of Technology. Her research interests lie primarily in data privacy, with connections to machine learning, algorithmic economics, optimization, statistics, and public policy. 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, a DARPA Young Faculty Award, an 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 and the Future of Privacy Forum's Advisory Board.
For more information about the workshop: events.withgoogle.com/2021-workshop-on-federated-learning-and-analytics/#content
A Google TechTalk, presented by Rachel Cummings, 2021/11/8
ABSTRACT: Mean Estimation with User-level Privacy under Data Heterogeneity (joint work with Vitaly Feldman, Audra McMillan, and Kunal Talwar).
A key challenge for data analysis in the federated setting is that user data is heterogeneous, i.e., it cannot be assumed to be sampled from the same distribution. Further, in practice, different users may possess vastly different number of samples. In this work we propose a simple model of heterogeneous user data that differs in both distribution and quantity of data, and we provide a method for estimating the population-level mean while preserving user-level differential privacy. We demonstrate asymptotic optimality of our estimator within a natural class of private estimators and also prove general lower bounds on the error achievable in our problem. We will conclude with a discussion of future challenges and possible extensions for learning from heterogeneous populations in the federated setting.
About the Speaker: Rachel Cummings, Columbia University
Dr. Rachel Cummings is an Assistant Professor of Industrial Engineering and Operations Research at Columbia University. Before joining Columbia, she was an Assistant Professor of Industrial and Systems Engineering and (by courtesy) Computer Science at the Georgia Institute of Technology. Her research interests lie primarily in data privacy, with connections to machine learning, algorithmic economics, optimization, statistics, and public policy. 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, a DARPA Young Faculty Award, an 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 and the Future of Privacy Forum's Advisory Board.
For more information about the workshop: events.withgoogle.com/2021-workshop-on-federated-learning-and-analytics/#content
