The effects of filtered video on awareness and privacy

Boyle, Michael; Edwards, C.; Greenberg, Saul

doi:10.1145/358916.358935

Cited by 213 publications

(154 citation statements)

References 16 publications

(32 reference statements)

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“…On the other hand, working at home can easily cut one off from the rest of a (distributed or co-located) work group if no awareness support is provided. (p. 250) Hudson and Smith offered various technical solutions, such as the blurring of the video images so that people's presence could be noticed but not their specific activities (see also Boyle, Edwards, & Greenberg, 2000).…”

Section: Introductionmentioning

confidence: 99%

The Watcher and the Watched: Social Judgments about Privacy in a Public Place

Friedman

Kahn

Hagman³

et al. 2009

Computer Supported Cooperative Work

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Digitally capturing and displaying real-time images of people in public places raises concerns for individual privacy. Applying principles of Value Sensitive HUMAN-COMPUTER INTERACTION, 2006, Volume 21, pp. 235-272 Copyright © 2006 Batya Friedman is a computer scientist and designer with an interest in Value Sensitive Design; she is a Professor in the Information School and Adjunct Professor in the Department of Computer Science and Engineering at the University of Washington. Peter H. Kahn Jr. is a developmental psychologist with interests that lie at the intersection of technology, nature, and social and moral development; he is an Associate Professor in the Department of Psychology and Adjunct Associate Professor in the Information School at the University of Washington. Jennifer Hagman is an information scientist with an interest in the role of informed consent in human computer interactions; she was a Research Analyst in the Information School at the University of Washington at the time of this research. Rachel L. Severson is a developmental psychologist with an interest in subject-other relations; she is a PhD student in the Department of Psychology at the University of Washington. Brian Gill is a statistician with an interest in applications of statistics in social and health sciences; he is an Associate Professor in the Department of Mathematics at Seattle Pacific University. Design, we conducted two studies of people's social judgments about this topic. In Study I, 750 people were surveyed as they walked through a public plaza that was being captured by a HDTV camera and displayed in real-time in the office of a building overlooking the plaza. In Study II, 120 individuals were interviewed about the same topic. Moreover, Study II controlled for whether the participant was a direct stakeholder of the technology (inside the office watching people on the HDTV large-plasma display window) or an indirect stakeholder (being watched in the public venue). Taking both studies together, results showed the following: (a) the majority of participants upheld some modicum of privacy in public; (b) people's privacy judgments were not a one-dimensional construct, but often involved considerations based on physical harm, psychological wellbeing, and informed consent; and (c) more women than men expressed concerns about the installation, and, unlike the men, equally brought forward their concerns whether they were The Watcher or The Watched. 236FRIEDMAN ET AL.

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Section: Introductionmentioning

confidence: 99%

The Watcher and the Watched: Social Judgments about Privacy in a Public Place

Friedman

Kahn

Hagman³

et al. 2009

Computer Supported Cooperative Work

View full text Add to dashboard Cite

show abstract

“…the eyes) or blurring or pixelation of faces [7,17,20,36] were eventually tested. However, these methods are visually intrusive and target human and algorithmic recognition alike.…”

Section: Related Workmentioning

confidence: 99%

Achieving Anonymity against Major Face Recognition Algorithms

Driessen

Dürmuth

2013

Communications and Multimedia Security

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Abstract. An ever-increasing number of personal photos is stored online. This trend can be problematic, because face recognition software can undermine user privacy in unexpected ways. Face de-identification aims to prevent automatic recognition of faces thus improving user privacy, but previous work alters the image in a way that makes them indistinguishable for both computers and humans, which prevents a wide-spread use.We propose a method for de-identification of images that effectively prevents face recognition software (using the most popular and effective algorithms) from identifying people, but still allows human recognition. We evaluate our method experimentally by adapting the CSU framework and using the FERET database. We show that we are able to achieve strong de-identification while maintaining reasonable image quality.

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“…Boyle et al [20] showed that blur filtration balances privacy and awareness for day-to-day office situations. Neustaedter et al [19] showed that blur filtration is insufficient to provide an adequate level of privacy for risky home situations.…”

Section: Related Workmentioning

confidence: 99%

Person De-identification in Videos

Agrawal

Narayanan

2010

Computer Vision – ACCV 2009

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Abstract-Advances in cameras and web technology have made it easy to capture and share large amounts of video data over to a large number of people. A large number of cameras oversee public and semi-public spaces today. These raise concerns on the unintentional and unwarranted invasion of the privacy of individuals caught in the videos. To address these concerns, automated methods to de-identify individuals in these videos are necessary. De-identification does not aim at destroying all information involving the individuals. Its ideal goals are to obscure the identity of the actor without obscuring the action. This paper outlines the scenarios in which de-identification is required and the issues brought out by those. We also present an approach to de-identify individuals from videos. Our approach involves tracking and segmenting individuals in a conservative voxel space involving x, y, and time. A de-identification transformation is applied per frame using these voxels to obscure the identity. Face, silhouette, gait, and other characteristics need to be obscured, ideally. We show results of our scheme on a number of videos and for several variations of the transformations. We present the results of applying algorithmic identification on the transformed videos. We also present the results of a user-study to evaluate how well humans can identify individuals from the transformed videos.

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The effects of filtered video on awareness and privacy

Cited by 213 publications

References 16 publications

The Watcher and the Watched: Social Judgments about Privacy in a Public Place

The Watcher and the Watched: Social Judgments about Privacy in a Public Place

Achieving Anonymity against Major Face Recognition Algorithms

Person De-identification in Videos

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