In many domains such as finance and medicine, organizations have encountered obstacles in data acquisition because their target applications need sensitive data that reside across multiple parties. However, such data cannot be shared today due to data privacy concerns, policy regulation, and business competition. My graduate research focused on solving this problem by enabling organizations to run complex computations on the joint dataset without revealing their sensitive input to the other parties. My overall approach is to co-design systems with cryptography to build practical and functional systems that provide strong and provable security. In this talk, I will focus on two systems — Opaque and Helen — which secure SQL analytics and machine learning, respectively. My open source has been used by organizations such as IBM Research, Ericsson, Alibaba, and Microsoft.
Wenting Zheng is a Ph.D. candidate at UC Berkeley, co-advised by Raluca Ada Popa and Ion Stoica. She completed her bachelor’s and master of engineering at MIT, where she was advised by Barbara Liskov. Her research interests are in computer systems, security, and applied cryptography. She is the recipient of a Berkeley Fellowship from 2014-2016, an IBM Research fellowship from 2017-2018, and was an invited participant at the 2019 EECS Rising Stars workshop.
Faculty Host: Adam Bates