Aragorn: A Privacy-Enhancing System for Mobile Cameras

被引:0
|
作者
Venugopalan, Hari [1 ]
Din, Zainul Abi [1 ]
Carpenter, Trevor [1 ]
Lowe-Power, Jason [1 ]
King, Samuel T. [1 ]
Shafiq, Zubair [1 ]
机构
[1] Univ Calif Davis, 1 Shields Ave, Davis, CA 95616 USA
基金
美国国家科学基金会;
关键词
Knowledge Distillation; Object Detection;
D O I
10.1145/3631406
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
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页数:31
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