Abdulmajeed Alqhatani, Heather R. Lipford (University of North Carolina at Charlotte)

Users of wearable fitness devices share different pieces of information with a variety of recipients to support their health and fitness goals. Device platforms could ease this sharing and empower users to protect their information by providing controls and features centered around these common sharing goals. However, there is little research that examines existing mechanisms for sharing and privacy management, and what needs users have beyond their current controls. In this paper, we analyze five popular wearable device platforms to develop taxonomies of mechanisms based on common sharing patterns and boundaries, as well as data collection awareness. With this analysis, we identify design opportunities for supporting users’ sharing and privacy needs.

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POSEIDON: Privacy-Preserving Federated Neural Network Learning

Sinem Sav (EPFL), Apostolos Pyrgelis (EPFL), Juan Ramón Troncoso-Pastoriza (EPFL), David Froelicher (EPFL), Jean-Philippe Bossuat (EPFL), Joao Sa Sousa (EPFL), Jean-Pierre Hubaux (EPFL)

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Vision-Based Two-Factor Authentication & Localization Scheme for Autonomous Vehicles

Anas Alsoliman, Marco Levorato, and Qi Alfred Chen (UC Irvine)

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ALchemist: Fusing Application and Audit Logs for Precise Attack...

Le Yu (Purdue University), Shiqing Ma (Rutgers University), Zhuo Zhang (Purdue University), Guanhong Tao (Purdue University), Xiangyu Zhang (Purdue University), Dongyan Xu (Purdue University), Vincent E. Urias (Sandia National Laboratories), Han Wei Lin (Sandia National Laboratories), Gabriela Ciocarlie (SRI International), Vinod Yegneswaran (SRI International), Ashish Gehani (SRI International)

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