You Can't See Me: Providing Privacy in Vision Pipelines via Wi-Fi Localization
You Can't See Me: Providing Privacy in Vision Pipelines via Wi-Fi Localization
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DOI：
10.1109/lanman58293.2023.10189418
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发表时间：
2023-07
期刊：
影响因子：
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通讯作者：
Shazal Irshad;Ria Thakkar;Eric Rozner;Eric Wustrow
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文献类型：
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作者：
Shazal Irshad;Ria Thakkar;Eric Rozner;Eric Wustrow
如今，摄像机无处不在。这些摄像机收集、流式传输、存储和分析视频片段，用于各种用例，包括监控、零售分析、建筑工程等。与此同时，许多公民对捕获的个人数据量沿着用于处理视频管道的算法和数据集感到厌倦。这项工作调查了用户如何通过明确同意被记录来选择退出这些管道。理想的系统应该混淆或以其他方式清除未经同意的用户数据，理想情况下甚至在用户进入视频处理管道之前。我们提出了一个系统，称为同意框，使混淆的用户，而不使用复杂的或个人识别的视觉技术。相反，经由用户的移动终端的Wi-Fi定位来估计用户在视频帧上的位置。这种估计使我们能够在帧进入复杂的视觉管道之前从帧中删除个体。
Today, video cameras are ubiquitously deployed. These cameras collect, stream, store, and analyze video footage for a variety of use cases, ranging from surveillance, retail analytics, architectural engineering, and more. At the same time, many citizens are becoming weary of the amount of personal data captured, along with the algorithms and datasets used to process video pipelines. This work investigates how users can opt-out of such pipelines by explicitly providing consent to be recorded. An ideal system should obfuscate or otherwise cleanse non-consenting user data, ideally before a user even enters the video processing pipeline itself. We present a system, called Consent-Box, that enables obfuscation of users without using complex or personally-identifying vision techniques. Instead, a user's location on a video frame is estimated via Wi-Fi localization of a user's mobile device. This estimation allows us to remove individuals from frames before those frames enter complex vision pipelines.