Visual privacy

Visual privacy describes the relationship between collection and dissemination of visual information, the expectation of privacy, and the legal issues surrounding them. These days cameras are ubiquitous. They are found in billons of electronic devices, ranging from smartphones to tablets, and laptops to surveillance cams in homes, business, and public.

Applications

Surveillance

However, privacy and trust implications surrounding it limit its ability to seamlessly blend into the computing environment. It is estimated that over 7 million CCTV cameras were deployed in the UK as of 2022.^[1] Camera networks have proliferated across other countries. Tools for controlling how these camera networks are used and modifications to the images and video sent to end-users have been explored.

Homes

At home, visual privacy is involved in protecting private spaces, in shared spaces, and protecting occupants from unwanted outsiders. It may also be a concern between residences without adequate screening.

Technologies enhancing visual privacy

Different technologies can preserve privacy while providing information from surveillance networks. Most of these solutions rely upon the target application to operate in a privacy-preserving manner:^[2]

"Respectful Cameras" automatically obscure the faces of observed people.^[3]
Google Streetview uses automatic face detection to blur faces.^[4]
Eptascape has a product that provides privacy-enabled surveillance.^[5]
Cardea is a context-aware visual privacy protection mechanism that protects bystanders' visual privacy in photos according to their context-dependent privacy preferences.^[6]
Thermal and depth cameras^[7] are used in person detection and people counting.
Privacy-preserving lens design^[8] consists of the joint optimization of optics and algorithms to perform vision tasks like human pose estimation and action recognition.
Edge computing: various applications enhance user privacy by keeping visual and other data on personal devices rather than sending to a server for processing. The latter increases the "surface", creating more chances for allowing others access to sensitive private data by service providers and/or malware.

References

^ "How Many CCTV Cameras in London? UK CCTV Numbers (Updated 2022)". Clarion UK. 2022-10-04. Retrieved 2024-06-18.
^ Koelle, Marion; Wolf, Katrin; Boll, Susanne (2018). "Beyond LED Status Lights - Design Requirements of Privacy Notices for Body-worn Cameras". Proceedings of the Twelfth International Conference on Tangible, Embedded, and Embodied Interaction. Tei '18. Stockholm, Sweden: ACM Press. pp. 177–187. doi:10.1145/3173225.3173234. ISBN 9781450355681. S2CID 3954480.
^ Jeremy Schiff; Marci Meingast; Deirdre K. Mulligan; Shankar Sastry; Ken Goldberg (2007). "Respectful Cameras: Detecting Visual Markers in Real-Time to Address Privacy Concerns". International Conference on Intelligent Robots and Systems (IROS). San Diego, California. October 2007.
^ "Street View revisits Manhattan".
^ "Eptascape, Inc. MPEG-7 Video Analytics". www.eptascape.com. Archived from the original on 21 June 2008. Retrieved 13 January 2022.
^ "Cardea: Context–Aware Visual Privacy Protection for Photo Taking and Sharing" (PDF). Archived from the original (PDF) on 2018-11-08. Retrieved 2023-12-24.
^ Pittaluga, Francesco; Koppal, Sanjeev J. (June 2015). "Privacy preserving optics for miniature vision sensors". 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, MA, USA: IEEE. pp. 314–324. CiteSeerX 10.1.1.944.2193. doi:10.1109/CVPR.2015.7298628. ISBN 9781467369640. S2CID 14056410.
^ Hinojosa, Carlos; Niebles, Juan Carlos; Arguello, Henry (October 2021). "Learning Privacy-preserving Optics for Human Pose Estimation". 2021 IEEE International Conference on Computer Vision (ICCV). Virtual, USA: IEEE/CVF: 2573–2582.