Privacy by Design - Principles of Privacy-Aware Ubiquitous Systems
UbiComp '01 Proceedings of the 3rd international conference on Ubiquitous Computing
Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
Extracting places from traces of locations
Proceedings of the 2nd ACM international workshop on Wireless mobile applications and services on WLAN hotspots
Passive capture and ensuing issues for a personal lifetime store
Proceedings of the the 1st ACM workshop on Continuous archival and retrieval of personal experiences
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The language of privacy: Learning from video media space analysis and design
ACM Transactions on Computer-Human Interaction (TOCHI)
Prototypes and Paratypes: Designing Mobile and Ubiquitous Computing Applications
IEEE Pervasive Computing
Encountering SenseCam: personal recording technologies in everyday life
Proceedings of the 11th international conference on Ubiquitous computing
Living in a glass house: a survey of private moments in the home
Proceedings of the 13th international conference on Ubiquitous computing
Parent-driven use of wearable cameras for autism support: a field study with families
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
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First-person point-of-view (FPPOV) images taken by wearable cameras can be used to better understand people's eating habits. Human computation is a way to provide effective analysis of FPPOV images in cases where algorithmic approaches currently fail. However, privacy is a serious concern. We provide a framework, the privacy-saliency matrix, for understanding the balance between the eating information in an image and its potential privacy concerns. Using data gathered by 5 participants wearing a lanyard-mounted smartphone, we show how the framework can be used to quantitatively assess the effectiveness of four automated techniques (face detection, image cropping, location filtering and motion filtering) at reducing the privacy-infringing content of images while still maintaining evidence of eating behaviors throughout the day.