There is growing recognition that machine learning (ML) exposes new security and privacy vulnerabilities in software systems, yet the technical community's understanding of the nature and extent of these vulnerabilities remains limited but expanding.
In this talk, we explore the threat model space of ML algorithms through the lens of Saltzer and Schroeder's principles for the design of secure computer systems. This characterization of the threat space prompts an investigation of current and future research directions.
Nicolas Papernot is an Assistant Professor at the University of Toronto and a Canada CIFAR AI Chair at the Vector Institute. Previously, he was a research scientist at Google Brain. His research interests span the security and privacy of machine learning. Nicolas received a best paper award at ICLR 2017. He is also the co-author of CleverHans, an open-source library widely adopted in the technical community to benchmark machine learning in adversarial settings, and TF Privacy, an open-source library for training differentially private models.
He serves on the program committees of several conferences including ACM CCS, IEEE S&P, and USENIX Security. He earned his Ph.D. at the Pennsylvania State University, working with Prof. Patrick McDaniel and supported by a Google PhD Fellowship.