{{Short description|Collection and dissemination of visual information and the expectation of privacy}}{{Original research|date=June 2008}}
'''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.<ref name="r243">{{cite web | title=How Many CCTV Cameras in London? UK CCTV Numbers (Updated 2022) | website=Clarion UK | date=2022-10-04 | url=https://clarionuk.com/resources/how-many-cctv-cameras-are-in-london/ | access-date=2024-06-18}}</ref> 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:<ref>{{Cite book |last1=Koelle |first1=Marion |title=Proceedings of the Twelfth International Conference on Tangible, Embedded, and Embodied Interaction |last2=Wolf |first2=Katrin |last3=Boll |first3=Susanne |date=2018 |publisher=ACM Press |isbn=9781450355681 |series=Tei '18 |location=Stockholm, Sweden |pages=177–187 |chapter=Beyond LED Status Lights - Design Requirements of Privacy Notices for Body-worn Cameras |doi=10.1145/3173225.3173234 |chapter-url=http://dl.acm.org/citation.cfm?doid=3173225.3173234 |s2cid=3954480}}</ref>
* "Respectful Cameras" automatically obscure the faces of observed people.<ref>{{cite journal|url=http://www.cs.berkeley.edu/~jschiff/RespectfulCameras/index.html |title=Respectful Cameras: Detecting Visual Markers in Real-Time to Address Privacy Concerns|author1=Jeremy Schiff | author2=Marci Meingast| author3=Deirdre K. Mulligan|author4=Shankar Sastry|author5=Ken Goldberg|journal=International Conference on Intelligent Robots and Systems (IROS). San Diego, California. October 2007|year=2007}}</ref> * Google Streetview uses automatic face detection to blur faces.<ref>{{Cite web|url=https://google-latlong.blogspot.com/2008/05/street-view-revisits-manhattan.html|title=Street View revisits Manhattan}}</ref> * Eptascape has a product that provides privacy-enabled surveillance.<ref>{{cite web |url=http://www.eptascape.com/ |title=Eptascape, Inc. MPEG-7 Video Analytics |website=www.eptascape.com |access-date=13 January 2022 |archive-url=https://web.archive.org/web/20080621124054/http://www.eptascape.com/ |archive-date=21 June 2008 |url-status=dead}}</ref> * Cardea is a context-aware visual privacy protection mechanism that protects bystanders' visual privacy in photos according to their context-dependent privacy preferences.<ref name="cardea">{{cite web| url=https://home.cse.ust.hk/~jshuaa/papers/mmsys18_cardea.pdf | access-date=2023-12-24 | archive-url=https://web.archive.org/web/20181108184408/https://home.cse.ust.hk/~jshuaa/papers/mmsys18_cardea.pdf | archive-date=2018-11-08| title=Cardea: Context–Aware Visual Privacy Protection for Photo Taking and Sharing}}</ref> * Thermal and depth cameras<ref>{{Cite book|last1=Pittaluga|first1=Francesco|last2=Koppal|first2=Sanjeev J.|title=2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) |chapter=Privacy preserving optics for miniature vision sensors |date=June 2015|location=Boston, MA, USA|publisher=IEEE|pages=314–324|doi=10.1109/CVPR.2015.7298628|isbn=9781467369640|citeseerx=10.1.1.944.2193|s2cid=14056410}}</ref> are used in person detection and people counting. * Privacy-preserving lens design<ref>{{Cite book|last1=Hinojosa|first1=Carlos|last2=Niebles|first2=Juan Carlos|last3=Arguello|first3=Henry|chapter=Learning Privacy-preserving Optics for Human Pose Estimation |date=October 2021|title=2021 IEEE/CVF International Conference on Computer Vision (ICCV) |publisher=IEEE/CVF|pages=2573–2582|doi=10.1109/ICCV48922.2021.00257 |isbn=978-1-6654-2812-5 }}</ref> 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.
==See also== * Mass surveillance
==References== {{reflist}}
==External links== * [https://www.law.berkeley.edu/institutes/bclt/events/unblinking/unblink.html Unblinking: New Perspectives on Visual Privacy in the 21st Century]
Category:Privacy