Nina Taft
Nina Taft is a Principal Research Scientist at Google where she leads the Applied Privacy Research group. Prior to joining Google, Nina worked at Technicolor Research, Intel Labs Berkeley, Sprint Labs and SRI. She received her PhD from UC Berkeley. Over the years, she has worked in the fields of networking protocols, network traffic matrix estimation, Internet traffic modeling and prediction, and intrusion detection. Most recently, her interests lie in applications of machine learning for privacy, developing privacy assistance both for users and developers, and understanding users via large-scale text analyses. She has been the chair or co-chair of the SIGCOMM, IMC and PAM conferences. (While some papers are listed here, see Google Scholar for a complete listing.)
Authored Publications
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A Decade of Privacy-Relevant Android App Reviews: Large Scale Trends
Omer Akgul
Michelle Mazurek
Benoit Seguin
2024
Unveiling Privacy Perspectives about Mobile Health Apps on a Large Scale
PETS workshop: Privacy, Safety and Trust for Mobile Health Apps (2024)
Balancing Privacy and Serendipity in CyberSpace
M. Satyanarayanan
Nigel Davies
International Workshop on Mobile Computing Systems and Applications (ACM HotMobile), http://www.hotmobile.org/2022/ (2022)
Analyzing User Perspectives on Mobile App Privacy at Scale
International Conference on Software Engineering (ICSE) (2022)
Hark: A Deep Learning System for Navigating Privacy Feedback at Scale
Rishabh Khandelwal
2022 IEEE Symposium on Security and Privacy (SP)
A Large Scale Study of Users Behaviors, Expectations and Engagement with Android Permissions
Weicheng Cao
Chunqiu Xia
David Lie
Lisa Austin
Usenix Security Symposium, Usenix, https://www.usenix.org/conference/usenixsecurity21 (2021)
"Shhh...be Quiet!" Reducing the Unwanted Interruptions of Notification Permission Prompts on Chrome
Balazs Engedy
Jud Porter
Kamila Hasanbega
Andrew Paseltiner
Hwi Lee
Edward Jung
PJ McLachlan
Jason James
30th USENIX Security Symposium (USENIX Security 21), USENIX Association, Vancouver, B.C. (2021)
Reducing Permission Requests in Mobile Apps
Martin Pelikan
Giles Hogben
Proceedings of ACM Internet Measurement Conference (IMC) (2019)
Exploring decision making with Android's runtime permission dialogs using in-context surveys
Thirteenth Symposium on Usable Privacy and Security (SOUPS), Usenix (2017)
