Bargav Jayaraman | Security Research Group

USENIX Security Symposium 2019

26 August 2019 privacy, privacy-preserving machine learning, Bargav Jayaraman, Sam Havron, Serge Egelman

Bargav Jayaraman presented our paper on Evaluating Differentially Private Machine Learning in Practice at the 28th USENIX Security Symposium in Santa Clara, California. Summary by Lea Kissner: Hey it's the results! pic.twitter.com/ru1FbkESho — Lea Kissner (@LeaKissner) August 17, 2019 Also, great to see several UVA folks at the conference including: Sam Havron (BSCS 2017, now a PhD student at Cornell) presented a paper on the work he and his colleagues have done on computer security for victims of intimate partner violence.

NeurIPS 2018: Distributed Learning without Distress

8 December 2018 privacy-preserving machine learning, secure computation, Bargav Jayaraman

Bargav Jayaraman presented our work on privacy-preserving machine learning at the 32nd Conference on Neural Information Processing Systems (NeurIPS 2018) in Montreal. Distributed learning (sometimes known as federated learning) allows a group of independent data owners to collaboratively learn a model over their data sets without exposing their private data. Our approach combines differential privacy with secure multi-party computation to both protect the data during training and produce a model that provides privacy against inference attacks.