阿拉贡

IF 3.6 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Harish Venugopalan, Z. Din, Trevor Carpenter, Jason Lowe-Power, Samuel T. King, Zubair Shafiq
{"title":"阿拉贡","authors":"Harish Venugopalan, Z. Din, Trevor Carpenter, Jason Lowe-Power, Samuel T. King, Zubair Shafiq","doi":"10.1145/3631406","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":20553,"journal":{"name":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","volume":"4 4","pages":"1 - 31"},"PeriodicalIF":3.6000,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Aragorn\",\"authors\":\"Harish Venugopalan, Z. Din, Trevor Carpenter, Jason Lowe-Power, Samuel T. King, Zubair Shafiq\",\"doi\":\"10.1145/3631406\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":20553,\"journal\":{\"name\":\"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies\",\"volume\":\"4 4\",\"pages\":\"1 - 31\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2024-01-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3631406\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3631406","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

移动应用程序开发人员通常依靠摄像头来实现丰富的功能。然而,当相机帧捕捉到应用程序功能所不需要的敏感信息时,让应用程序不受限制地访问移动摄像头就会对隐私构成威胁。为了减轻这种威胁,我们推出了 Aragorn,这是一种新颖的隐私增强型移动摄像头系统,可在应用程序访问摄像头之前对摄像头帧中的信息进行细粒度控制。Aragorn 通过检测对应用程序功能至关重要的区域,自动对相机帧进行净化,并屏蔽其他所有信息,从而在保护隐私的同时保留应用程序的实用性。Aragorn 可以满足各种相机应用程序的需求,并结合知识提炼和众包,为以前不支持的应用程序提供强大的支持。在评估中,我们发现在不降低实用性的情况下,Aragorn 在信用卡扫描和人脸识别方面的检测准确率分别为 89%和 100%。我们还发现,Aragorn 在安卓相机子系统中的实现仅导致帧速率平均每秒下降 0.01 帧。我们的评估结果表明,Aragorn 对系统性能的影响是合理的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Aragorn
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Computer Science-Computer Networks and Communications
CiteScore
9.10
自引率
0.00%
发文量
154
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信