Friday, October 11, 2024 
10:20 – 11:10 a.m. (CST)
ETB 1020
Dr. Nina Taft
Principal Scientist/Director at Google
Title: “Leveraging Deep Learning to Understand Users’ Views about Privacy”
Abstract
Privacy nudges can offer developers suggestions to improve the privacy of their apps. We design a multi-stage methodology that leverages recent advances in NLP and LLMs to automatically extract privacy insights from smartphone app reviews. Our analysis pipeline includes a privacy classifier, automated issue tagging for thematic clusters, a classifier to attach emotions to reviews, and extracts temporal and geographic trends. We apply this methodology to publicly visible app reviews on the Google Play store that span a 10-year period and uncover 12 million instances of privacy-relevant reviews. We’ll summarize users’ perspectives about smartphone app privacy along multiple dimensions – across a decade of time, from over 200 countries, and across a diversity of app types and privacy topics. This approach complements traditional user studies by providing developers with actionable feedback from a vast and diverse user base.
Biography
Nina Taft is a Principal Scientist/Director at Google where she leads the Applied Privacy Research group. Nina received her PhD from UC Berkeley and has worked in industrial research labs since then – at SRI, Sprint Labs, Intel Berkeley Labs, and Technicolor Research before joining Google. For many years, Nina worked in the field of networking, focused on Internet traffic modeling, traffic matrix estimation, and intrusion detection. In 2017, she received the top-10 women in networking IEEE N2Women award. In the last decade, she has been working on privacy enhancing technologies with a focus on applications of machine learning for privacy. She has been chair of the SIGCOMM, IMC and PAM conferences, has published over 100 papers, and holds 10 patents.
Please join us on Friday, 10/11/24 at 10:20 a.m. in ETB 1020!