Everyday, millions of people take pictures with their mobile or wearable cameras in urban spaces. Many of these shared pictures are then uploaded online. How can unintentionally photographed bystanders of these cameras express and enforce their privacy preferences?
Due to the ongoing proliferation of mobile and wearable devices with integrated cameras, the problem of unintentionally photographed individuals in public spaces amplifies. These new technologies are becoming more and more pervasive and therefore pose new challenges for protecting the privacy of their users and bystanders. At this time, no feasible solution is available to get an informed consent between the photographer and all the individuals on the picture. In order to fill this analog gap, we propose the P3F framework, a usable and easy-to-understand way that allows individuals to encode their personal privacy policies in clothing to communicate self-chosen restrictions towards these cameras. Our approach is based on computer vision only and therefore does not require its users to buy additional technology. In the course of this talk, I will present the P3F framework and furthermore discuss how we must re-think the design of privacy-enhancing technology to fill the analog gap between the photographer and unintentionally photographed bystanders.