Song Liao (Texas Tech University), Jingwen Yan (Clemson University), Yichen Liu (University of Illinois Urbana-Champaign), David Kotz (Dartmouth College), Luyi Xing (University of Illinois Urbana-Champaign), Long Cheng (Clemson University)

Mobile apps may collect, share, and analyze data from users. Although users can choose to decline apps’ data collection behaviors through mobile permission systems or in-app settings, it is challenging and time-consuming for users to manually discover and correctly configure all the privacy settings for apps on their mobile phones. This issue also occurs in IoT apps, where users need to configure each device separately. Although they can manage some settings with platform apps (like Apple Home), many IoT devices expose device-specific settings within a device specific app. In this position paper, we propose the PRIVACYPROFILE, a framework that allows users to easily set their global privacy preferences and apply them to apps automatically. Users can indicate whether each of their privacy-related information can be collected, shared, and analyzed in their profile. Compatible apps then read the privacy profile and automatically configure their settings for users, e.g., enabling data collection behaviors or disabling data sharing. This design enables users to easily configure their privacy preferences once, rather than having to manually open each app and locate the corresponding privacy settings.

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Memory Backdoor Attacks on Neural Networks

Eden Luzon (Ben Gurion University of the Negev), Guy Amit (Ben-Gurion University & IBM Research), Roy Weiss (Ben Gurion University of the Negev), Torsten Krauß (University of Würzburg), Alexandra Dmitrienko (University of Würzburg), Yisroel Mirsky (Ben Gurion University of the Negev)

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The Things That Count: Coverage Evaluation Under the Microscope...

Tobias Holl (Ruhr University Bochum), Leon Weiß (Ruhr University Bochum), Kevin Borgolte (Ruhr University Bochum)

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Towards Bridging the Telemetry Gap for Security Applications in...

Haohuang Wen (The Ohio State University and SE-RAN.ai), Vinod Yegneswaran (SRI and SE-RAN.ai), Phillip Porras (SRI and SE-RAN.ai), Ashish Gehani (SRI and SE-RAN.ai), Prakhar Sharma (SRI and SE-RAN.ai), Zhiqiang Lin (The Ohio State University and SE-RAN.ai)

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