Differential Privacy at Scale: Uber and Berkeley Collaboration

Tuesday, January 16, 2018 - 11:00 am11:30 am

Joe Near, Postdoctoral Researcher, University of California, Berkeley

Abstract: 

Much of the research in differential privacy to date falls short of private industry requirements in its ability to scale and data-proven success. However, most researchers do not have access to real-world data to prove new techniques. In this talk, Uber's privacy engineering team and Berkeley researchers discuss the story behind their pragmatic collaboration and how it led to multiple open source releases from their differential privacy stack.

Joe Near, Postdoctoral Researcher, University of California, Berkeley

Joe Near is a postdoctoral researcher in Dawn Song's group at UC Berkeley. His research interests include security, data privacy, program analysis and programming languages. As part of his PhD work at MIT, where he was the recipient of an NSF Graduate Research Fellowship, Joe worked on static techniques for uncovering access control bugs in web applications.

BibTeX
@inproceedings {208167,
author = {Joe Near},
title = {Differential Privacy at Scale: Uber and Berkeley Collaboration},
booktitle = {Enigma 2018 (Enigma 2018)},
year = {2018},
address = {Santa Clara, CA},
url = {https://www.usenix.org/node/208168},
publisher = {{USENIX} Association},
}