Negotiating Privacy Preferences in Video Surveillance Systems

Barhm, Mukhtaj S.; Qwasmi, Nidal; Qureshi, Faisal Z.; El-Khatib, Khalil

doi:10.1007/978-3-642-21827-9_52

Cited by 10 publications

(3 citation statements)

References 8 publications

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“…Finally, PAAS (Barhm et al, 2011) is a privacy-aware surveillance system that enables monitored subjects to specify their privacy preferences via gestures. It uses face recognition technology in order to map individuals to their privacy preferences and security credentials that are encoded using an extension of the P3P-APPEL framework (Cranor et al, 2002).…”

Section: Privacy-aware Intelligent Monitoring Systemsmentioning

confidence: 99%

Visual privacy protection methods: A survey

Padilla-López

Chaaraoui

Flórez-Revuelta

2015

Expert Systems with Applications

158

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Recent advances in computer vision technologies have made possible the development of intelligent monitoring systems for video surveillance and ambientassisted living. By using this technology, these systems are able to automatically interpret visual data from the environment and perform tasks that would have been unthinkable years ago. These achievements represent a radical improvement but they also suppose a new threat to individual's privacy. The new capabilities of such systems give them the ability to collect and index a huge amount of private information about each individual. Next-generation systems have to solve this issue in order to obtain the users' acceptance. Therefore, there is a need for mechanisms or tools to protect and preserve people's privacy. This paper seeks to clarify how privacy can be protected in imagery data, so as a main contribution a comprehensive classification of the protection methods for visual privacy as well as an up-to-date review of them are provided. A survey of the existing privacy-aware intelligent monitoring systems and a valuable discussion of important aspects of visual privacy are also provided.

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Section: Privacy-aware Intelligent Monitoring Systemsmentioning

confidence: 99%

Visual privacy protection methods: A survey

Padilla-López

Chaaraoui

Flórez-Revuelta

2015

Expert Systems with Applications

158

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“…Privacy Aware Surveillance System (PASS) proposed by Barhm et. al [30] employs video analysis to locate and identify individuals present in the scene. Individuals interact with PASS and specify their preferences via gestures.…”

Section: Intervention Tokens and Privacy Policiesmentioning

confidence: 99%

The privacy challenges of in-depth video analytics

Piatrik

Fernández

Izquierdo

2012

2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP)

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The increasing need for both automated and privacy-respecting CCTV systems adds many challenges to the tasks of Video Analytics. The growing capabilities of automated surveillance systems lead to the automatic extraction and processing of complex and potentially personal data related to individuals who may not even be aware of it. This paper discusses the issues related to the processing of potentially sensitive information extracted from various computer vision algorithms and techniques such as person tracking, soft biometrics, behaviour analysis or multi-modal person identification, and delivers insights regarding technical solutions and guidelines for achieving a higher level of privacy compliance.

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“…Additionally, some respondents wanted activity-based controls, while others wanted location-based controls. An effective system that allowed bystanders to control their privacy for vehicle-based recording might include controls that combined a number of factors, including location, time, and allowing individuals to opt out [4].…”

Section: Trustworthy and Usable Privacy Preservationmentioning

confidence: 99%

Attitudes toward vehicle-based sensing and recording

Sleeper

Schnorf

Kemler

et al. 2015

Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

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Vehicles increasingly include features that rely on hi-tech sensors and recording; however, little is known of public attitudes toward such recording. We use two studies, an online survey (n=349) and an interview-based study (n=15), to examine perceptions of vehicle-based sensing and recording. We focus on: 1) how vehicle-based recording and sensing may differ from perceptions of current recording; 2) factors that impact comfort with vehicle-based recording for hypothetical drivers versus bystanders; and 3) perceptions of potential privacy-preserving techniques. We find that vehiclebased recording challenges current mental models of recording awareness. Comfort tends to depend on perceived benefits, which can vary by stakeholder type. Perceived privacy in spaces near cars can also impact comfort and reflect mental models of private spaces as well as the range of potentially sensitive activities people perform in and near cars. Privacypreserving techniques may increase perceived comfort but may require addressing trust and usability issues.

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Negotiating Privacy Preferences in Video Surveillance Systems

Cited by 10 publications

References 8 publications

Visual privacy protection methods: A survey

Visual privacy protection methods: A survey

The privacy challenges of in-depth video analytics

Attitudes toward vehicle-based sensing and recording

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