Name
Abstract | ||
|---|---|---|
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 based 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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2638728.2641707 | UbiComp Adjunct |
Keywords | Field | DocType |
design,portable devices,privacy,security,usability,augmented reality,law | Computer science,Computer security,Usability,Popularity,Augmented reality,Human–computer interaction,Access control,Wearable technology,Privacy software | Conference |
Citations | PageRank | References |
27 | 1.12 | 13 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nisarg Raval | 1 | 68 | 5.85 |
| Landon P. Cox | 2 | 1396 | 109.41 |
| Animesh Srivastava | 3 | 50 | 3.54 |
| Ashwin Machanavajjhala | 4 | 2624 | 132.52 |
| Kiron Lebeck | 5 | 71 | 5.07 |