MarkIt

Nisarg Raval, Landon Cox, Animesh Srivastava, Ashwin Machanavajjhala, Kiron Lebeck
<span title="">2014</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/tbl7svy7dzb65af4vqu34wmt7i" style="color: black;">Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing Adjunct Publication - UbiComp &#39;14 Adjunct</a> </i> &nbsp;
The increasing popularity of wearable devices that continuously capture video, and the prevalence of third-party applications that utilize these feeds have resulted in a new threat to privacy. In many situations, sensitive objects/regions are maliciously (or accidentally) captured in a video frame by third-party applications. However, current solutions do not allow users to specify and enforce fine grained access control over video feeds. In this paper, we describe MarkIt, a computer vision
more &raquo; ... d privacy marker framework, that allows users to specify and enforce fine grained access control over video feeds. We present two example privacy marker systems -PrivateEye and WaveOff. We conclude with a discussion of the computer vision, privacy and systems challenges in building a comprehensive system for fine grained access control over video feeds.
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