Shikun Zhang, Norman Sadeh (Carnegie Mellon University)

Inspired by earlier academic research, iOS app privacy labels and the recent Google Play data safety labels have been introduced as a way to systematically present users with concise summaries of an app’s data practices. Yet, little research has been conducted to determine how well today’s mobile app privacy labels address people’s actual privacy concerns or questions. We analyze a crowd-sourced corpus of privacy questions collected from mobile app users to determine to what extent these mobile app labels actually address users’ privacy concerns and questions. While there are differences between iOS labels and Google Play labels, our results indicate that an important percentage of people’s privacy questions are not answered or only partially addressed in today’s labels. Findings from this work not only shed light on the additional fields that would need to be included in mobile app privacy labels but can also help inform refinements to existing labels to better address users’ typical privacy questions.

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Formally Verified Software Update Management System in Automotive

Jaewan Seo, Jiwon Kwak, Seungjoo Kim (Korea University)

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Machine Unlearning of Features and Labels

Alexander Warnecke (TU Braunschweig), Lukas Pirch (TU Braunschweig), Christian Wressnegger (Karlsruhe Institute of Technology (KIT)), Konrad Rieck (TU Braunschweig)

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Detection and Resolution of Control Decision Anomalies

Prof. Kang Shin (Kevin and Nancy O'Connor Professor of Computer Science, and the Founding Director of the Real-Time Computing Laboratory (RTCL) in the Electrical Engineering and Computer Science Department at the University of Michigan)

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