In the Information Age, visual media take on powerful new forms. Photographs once printed on paper and stored in physical albums now exist as digital files. With the rise of social media, photo data has moved to the cloud for rapid dissemination. The upside can be measured in terms of increased efficiency, greater reach, or reduced printing costs. But there is a downside that is harder to quantify: the risk of private photos or videos leaking inappropriately. Human imagery is potentially sensitive, revealing private details of a personís body, lifestyle, activities, and more. Images create visceral responses and have the potential to permanently damage a personís reputation.
We employed the theory of contextual integrity to explore privacy aspects of transmitting the human form. In response to privacy threats from new sociotechnical systems, we developed practical solutions that have the potential to restore balance. The main work is a set of client-side, technical interventions that can be used to alter information flows and provide features to support visual privacy. In the first approach, we use crowdsourcing to extract specific, useful human signal from video to decouple it from bundled identity information. The second approach is an attempt to achieve similar ends with pure software. Instead of using information workers, we developed a series of filters that alter video to hide identity information while still revealing motion signal. The final approach is an attempt to control the recipients of photos by encoding them in the visual channel. The software completely protects data from third-parties who lack proper credentials and maintains data integrity by exploiting the visual coherence of uploaded images, even in the face of JPEG compression. The software offers end-to-end encryption that is compatible with existing social media applications.