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

IsolateGPT: An Execution Isolation Architecture for LLM-Based Agentic Systems

Yuhao Wu (Washington University in St. Louis), Franziska Roesner (University of Washington), Tadayoshi Kohno (University of Washington), Ning Zhang (Washington University in St. Louis), Umar Iqbal (Washington University in St. Louis)

Read More

Rediscovering Method Confusion in Proposed Security Fixes for Bluetooth

Maximilian von Tschirschnitz (Technical University of Munich), Ludwig Peuckert (Technical University of Munich), Moritz Buhl (Technical University of Munich), Jens Grossklags (Technical University of Munich)

Read More

Rondo: Scalable and Reconfiguration-Friendly Randomness Beacon

Xuanji Meng (Tsinghua University), Xiao Sui (Shandong University), Zhaoxin Yang (Tsinghua University), Kang Rong (Blockchain Platform Division,Ant Group), Wenbo Xu (Blockchain Platform Division,Ant Group), Shenglong Chen (Blockchain Platform Division,Ant Group), Ying Yan (Blockchain Platform Division,Ant Group), Sisi Duan (Tsinghua University)

Read More

LLMPirate: LLMs for Black-box Hardware IP Piracy

Vasudev Gohil (Texas A&M University), Matthew DeLorenzo (Texas A&M University), Veera Vishwa Achuta Sai Venkat Nallam (Texas A&M University), Joey See (Texas A&M University), Jeyavijayan Rajendran (Texas A&M University)

Read More