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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Tactics, Threats & Targets: Modeling Disinformation and its Mitigation

Shujaat Mirza (New York University), Labeeba Begum (New York University Abu Dhabi), Liang Niu (New York University), Sarah Pardo (New York University Abu Dhabi), Azza Abouzied (New York University Abu Dhabi), Paolo Papotti (EURECOM), Christina Pöpper (New York University Abu Dhabi)

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BARS: Local Robustness Certification for Deep Learning based Traffic...

Kai Wang (Tsinghua University), Zhiliang Wang (Tsinghua University), Dongqi Han (Tsinghua University), Wenqi Chen (Tsinghua University), Jiahai Yang (Tsinghua University), Xingang Shi (Tsinghua University), Xia Yin (Tsinghua University)

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How to Count Bots in Longitudinal Datasets of IP...

Leon Böck (Technische Universität Darmstadt), Dave Levin (University of Maryland), Ramakrishna Padmanabhan (CAIDA), Christian Doerr (Hasso Plattner Institute), Max Mühlhäuser (Technical University of Darmstadt)

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