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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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Targeted Privacy Attacks by Fingerprinting Mobile Apps in LTE Radio Layer
We investigate the feasibility of targeted privacy attacks using only information available in physical channels of LTE mobile networks and propose three privacy attacks to demonstrate this feasibility: mobile-app fingerprinting attack, history attack, and correlation attack. These attacks can reveal the geolocation of targeted mobile devices, the victim's app usage patterns, and even the relationship between two users within the same LTE network cell. An attacker also may launch these attacks stealthily by capturing radio signals transmitted over the air, using only a passive sniffer as equipment. To ensure the impact of these attacks on mobile users' privacy, we perform evaluations in both laboratory and real-world settings, demonstrating their practicality and dependability. Furthermore, we argue that these attacks can target not only 4G/LTE but also the evolving 5G standards.
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- PAR ID:
- 10464844
- Editor(s):
- IEEE/IFIP
- Date Published:
- Journal Name:
- 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)
- Page Range / eLocation ID:
- 261 to 273
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/DSN58367.2023.00035
