Usability and privacy: a study of Kazaa P2P file-sharing
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Personal privacy through understanding and action: five pitfalls for designers
Personal and Ubiquitous Computing
Location disclosure to social relations: why, when, & what people want to share
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
User interfaces for privacy agents
ACM Transactions on Computer-Human Interaction (TOCHI)
Over-exposed?: privacy patterns and considerations in online and mobile photo sharing
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the 4th workshop on Embedded networked sensors
You've been warned: an empirical study of the effectiveness of web browser phishing warnings
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Nericell: rich monitoring of road and traffic conditions using mobile smartphones
Proceedings of the 6th ACM conference on Embedded network sensor systems
Laermometer: a mobile noise mapping application
Proceedings of the 5th Nordic conference on Human-computer interaction: building bridges
An activity recognition system for mobile phones
Mobile Networks and Applications
Strategies and struggles with privacy in an online social networking community
BCS-HCI '08 Proceedings of the 22nd British HCI Group Annual Conference on People and Computers: Culture, Creativity, Interaction - Volume 1
SoundSense: scalable sound sensing for people-centric applications on mobile phones
Proceedings of the 7th international conference on Mobile systems, applications, and services
Exploring Privacy Concerns about Personal Sensing
Pervasive '09 Proceedings of the 7th International Conference on Pervasive Computing
Four billion little brothers?: privacy, mobile phones, and ubiquitous data collection
Communications of the ACM - Scratch Programming for All
Visual vs. compact: a comparison of privacy policy interfaces
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Inference attacks on location tracks
PERVASIVE'07 Proceedings of the 5th international conference on Pervasive computing
The impact of social navigation on privacy policy configuration
Proceedings of the Sixth Symposium on Usable Privacy and Security
Crying wolf: an empirical study of SSL warning effectiveness
SSYM'09 Proceedings of the 18th conference on USENIX security symposium
Proceedings of the 12th ACM international conference on Ubiquitous computing
Unobtrusive User-Authentication on Mobile Phones Using Biometric Gait Recognition
IIH-MSP '10 Proceedings of the 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing
Activity recognition using cell phone accelerometers
ACM SIGKDD Explorations Newsletter
Bridging the Gap in Computer Security Warnings: A Mental Model Approach
IEEE Security and Privacy
TouchLogger: inferring keystrokes on touch screen from smartphone motion
HotSec'11 Proceedings of the 6th USENIX conference on Hot topics in security
A survey on privacy in mobile participatory sensing applications
Journal of Systems and Software
ACCessory: password inference using accelerometers on smartphones
Proceedings of the Twelfth Workshop on Mobile Computing Systems & Applications
Exploring user preferences for privacy interfaces in mobile sensing applications
Proceedings of the 11th International Conference on Mobile and Ubiquitous Multimedia
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Mobile phones are increasingly leveraged as sensor platforms to collect information about user's context. The collected sensor readings can however reveal personal and sensitive information about the users and hence put their privacy at stake. In prior work, we have proposed different user interfaces allowing users to select the degree of granularity at which the sensor readings are shared in order to protect their privacy. In this paper, we aim at further increasing user awareness about potential privacy risks and investigate the introduction of picture-based warnings based on their current privacy settings. Depending on their privacy conception and the proposed warnings, users can then adapt their settings or leave them unchanged. We evaluate the picture-based warnings by conducting a user study involving 30 participants. The results show that more than 70% of the participants would change their settings after having seen the picture-based warnings.