Aragorn: A Privacy-Enhancing System for Mobile Cameras

Local Download ACM DL Link

Mobile app developers often rely on cameras to implement rich features. However, giving apps unfettered access to the mobile camera poses a privacy threat when camera frames capture sensitive information that is not needed for the app’s functionality. To mitigate this threat, we present Aragorn, a novel privacy-enhancing mobile camera system that provides fine grained control over what information can be present in camera frames before apps can access them. Aragorn automatically sanitizes camera frames by detecting regions that are essential to an app’s functionality and blocking out everything else to protect privacy while retaining app utility. Aragorn can cater to a wide range of camera apps and incorporates knowledge distillation and crowdsourcing to extend robust support to previously unsupported apps. In our evaluations, we see that, with no degradation in utility, Aragorn detects credit cards with 89\% accuracy and faces with 100\% accuracy in context of credit card scanning and face recognition respectively. We show that Aragorn’s implementation in the Android camera subsystem only suffers an average drop of 0.01 frames per second in frame rate. Our evaluations show that the overhead incurred by Aragorn to system performance is reasonable.

Citation

Hari Venugopalan, Zainul Abi Din, Trevor Carpenter, Jason Lowe-Power, Samuel T. King, and Zubair Shafiq. 2024. Aragorn: A Privacy-Enhancing System for Mobile Cameras. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 7, 4, Article 181 (December 2023), 31 pages. https://doi.org/10.1145/3631406

@article{Venugopalan2024aragorn,
author = {Venugopalan, Hari and Din, Zainul Abi and Carpenter, Trevor and Lowe-Power, Jason and King, Samuel T. and Shafiq, Zubair},
title = {Aragorn: A Privacy-Enhancing System for Mobile Cameras},
year = {2024},
issue_date = {December 2023},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {7},
number = {4},
url = {https://doi.org/10.1145/3631406},
doi = {10.1145/3631406},
journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.},
month = {jan},
articleno = {181},
numpages = {31},
keywords = {Knowledge Distillation, Object Detection}
}