Xiaoyuan Wu (Carnegie Mellon University), Lydia Hu (Carnegie Mellon University), Eric Zeng (Carnegie Mellon University), Hana Habib (Carnegie Mellon University), Lujo Bauer (Carnegie Mellon University)

Apple's App Privacy Report (``privacy report''), released in 2021, aims to
inform iOS users about apps' access to their data and sensors (e.g., contacts,
camera) and, unlike other privacy dashboards, what domains are contacted by apps and websites. To evaluate the
effectiveness of the privacy report, we conducted semi-structured interviews
(textit{n} = 20) to examine users' reactions to the information, their understanding of relevant privacy
implications, and how they might change
their behavior to address privacy concerns. Participants easily understood which
apps accessed data and sensors at certain times on their phones, and knew how to
remove an app's permissions in case of unexpected access. In contrast,
participants had difficulty understanding apps' and websites' network
activities. They were confused about how and why network activities occurred,
overwhelmed by the number of domains their apps contacted, and uncertain about
what remedial actions they could take against potential privacy threats. While
the privacy report and similar tools can increase transparency by presenting
users with details about how their data is handled, we recommend providing more
interpretation or aggregation of technical details, such as the purpose of
contacting domains, to help users make informed decisions.

View More Papers

Uncovering the iceberg from the tip: Generating API Specifications...

Miaoqian Lin (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China; School of Cyber Security, University of Chinese Academy of Sciences, China), Kai Chen (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China; School of Cyber Security, University of Chinese Academy of Sciences, China), Yi Yang (Institute of Information Engineering, Chinese Academy of…

Read More

Revealing the Black Box of Device Search Engine: Scanning...

Mengying Wu (Fudan University), Geng Hong (Fudan University), Jinsong Chen (Fudan University), Qi Liu (Fudan University), Shujun Tang (QI-ANXIN Technology Research Institute; Tsinghua University), Youhao Li (QI-ANXIN Technology Research Institute), Baojun Liu (Tsinghua University), Haixin Duan (Tsinghua University; Quancheng Laboratory), Min Yang (Fudan University)

Read More

Statically Discover Cross-Entry Use-After-Free Vulnerabilities in the Linux Kernel

Hang Zhang (Indiana University Bloomington), Jangha Kim (The Affiliated Institute of ETRI, ROK), Chuhong Yuan (Georgia Institute of Technology), Zhiyun Qian (University of California, Riverside), Taesoo Kim (Georgia Institute of Technology)

Read More

URVFL: Undetectable Data Reconstruction Attack on Vertical Federated Learning

Duanyi Yao (Hong Kong University of Science and Technology), Songze Li (Southeast University), Xueluan Gong (Wuhan University), Sizai Hou (Hong Kong University of Science and Technology), Gaoning Pan (Hangzhou Dianzi University)

Read More