Talk Information 03/10/2022

Abstract: I will start this talk by overviewing federated learning and its core data minimization principles. I will then describe how privacy can be strengthened using complementary privacy techniques such as differential privacy, secure multi-party computation, and privacy auditing methods. I will spend much of the talk describing how we can carefully combine technologies like differential privacy and secure aggregation to obtain formal distributed privacy guarantees without fully trusting the server in adding noise. I will present a comprehensive end-to-end system, which appropriately discretizes the data and adds discrete Gaussian or Skellam noise before performing secure aggregation. I will conclude by showing experimental results that demonstrate that our solution is able to achieve a comparable accuracy to central differential privacy (which requires trusting the server in adding noise) with just 16 bits of precision per value. If time permits, I will highlight new work on combining linear compression schemes with secure aggregation and differential privacy to reduce the communication overhead down to less than 1 bit per value without sacrificing accuracy. 

Bio: Peter Kairouz is a research scientist at Google, where he coordinates research efforts on federated learning and privacy-preserving technologies. Before joining Google, he was a Postdoctoral Research Fellow at Stanford University. He received his Ph.D. in electrical and computer engineering from the University of Illinois at Urbana-Champaign (UIUC). He is the recipient of the 2012 Roberto Padovani Scholarship from Qualcomm’s Research Center, the 2015 ACM SIGMETRICS Best Paper Award, the 2021 ACM Conference on Computer and Communications Security (CCS) Best Paper Award, the 2015 Qualcomm Innovation Fellowship Finalist Award, and the 2016 Harold L. Olesen Award for Excellence in Undergraduate Teaching from UIUC.