Kassem Fawaz, an assistant professor of electrical and computer engineering at the University of Wisconsin-Madison, earned international recognition for his contributions to technologies that protect people’s online privacy.
At the annual Privacy Enhancing Technologies Symposium in Stockholm in July 2019, Fawaz and collaborators Hamza Harkous at the Swiss Federal Institute of Technology Lausanne as well as researchers from the University of Michigan, earned the 2019 Caspar Bowden Award for Outstanding Research in Privacy Enhancing Technologies.
The award recognizes their research, “Polisis: Automated analysis and presentation of privacy policies using deep learning,” which they first presented at the 2018 USENIX Security Conference. (A number of news stories highlighted the tool, including this story in Wired.)
“We believe that this technology will help in making users more aware about the privacy practices of the websites with which they interact,” says Fawaz.
The researchers created an automated tool called Polisis for analyzing online privacy policies—those often lengthy and confusingly written documents that many consumers all too often accept without understanding the ramifications of what personal data they might be giving away and how companies will use that data.
Polisis takes advantage of advanced machine learning techniques to comb through 130,000 privacy policies and develop a hierarchy of neural-network classifiers that can account for both high-level and fine-detail features of the documents.
The application guides users through complex legal minutiae in an engaging, colorful, easy-to-comprehend graphical format that lays out what types of data companies collect, how and why they share it, how they keep that information secure, what data they store, how data from children (via networked toys, for example) is treated, an overview of users’ options for controlling the use of their information, and more.
Author: Sam Million-Weaver