BLINDSPOT: Enabling Bystander-Controlled Privacy Signaling for Camera-Enabled Devices

Camera-equipped mobile devices, such as phones, smart glasses, and AR headsets, pose a privacy challenge for bystanders, who currently lack effective real-time mechanisms to control the capture of their picture, video, including their face. We present BlindSpot, an on-device system that enables bystanders to manage their own privacy by signaling their privacy preferences in real-time without previously sharing any sensitive information. Our main contribution is the design and comparative evaluation of three distinct signaling modalities: a hand gesture mechanism, a significantly improved visible light communication (VLC) protocol, and a novel ultra-wideband (UWB) communication protocol. For all these modalities, we also design a validation mechanism that uses geometric consistency checks to verify the origin of a signal relative to the sending bystander, and defend against impersonation attacks. We implement the complete system (BlindSpot) on a commodity smartphone and conduct a comprehensive evaluation of each modality's accuracy and latency across various distances, lighting conditions, and user movements. Our results demonstrate the feasibility of these novel bystander signaling techniques and their trade-offs in terms of system performance and convenience.