Unpacking "privacy" for a networked world
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Context-aware telephony: privacy preferences and sharing patterns
CSCW '06 Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work
Privacy-Aware Context Discovery for Next Generation Mobile Services
SAINT-W '07 Proceedings of the 2007 International Symposium on Applications and the Internet Workshops
Privacy in Location-Aware Computing Environments
IEEE Pervasive Computing
From awareness to repartee: sharing location within social groups
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Human-Computer Interaction
Understanding and capturing people's privacy policies in a mobile social networking application
Personal and Ubiquitous Computing
Empirical models of privacy in location sharing
Proceedings of the 12th ACM international conference on Ubiquitous computing
Proceedings of the 12th ACM international conference on Ubiquitous computing
Capturing location-privacy preferences: quantifying accuracy and user-burden tradeoffs
Personal and Ubiquitous Computing
UbiComp'05 Proceedings of the 7th international conference on Ubiquitous Computing
Privacy context model for dynamic privacy adaptation in ubiquitous computing
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Crowdsourcing privacy preferences in context-aware applications
Personal and Ubiquitous Computing
| Hi-index | 0.00 |
Context-awareness enables applications to better streamline and personalize their service according to the current situation of the user. However, the user's information used by context-aware applications, such as the user's current location, is inherently private and sensitive. Using this information without proper control by the user can lead to privacy risks and might harm the trust users have in the context-aware application. To address this tradeoff between the effectiveness and privacy, we present Super-Ego, a framework for at-hoc management of access to location information in ubiq-uitous environment. Using this framework, we model and evaluate different decision strategies for managing mobile application's access to location context. The strategies we test are based on automatic algorithms that use knowledge about historical disclosure of locations by large number of users, with the optional delegation of some of the decisions to the user. We evaluate the system empirically, using people's detailed location trails from public resources, augmented with simulated data about sharing behavior. Our results reflect on an interesting tradeoff between automation and accuracy, which can enable the design of efficient and usable approaches to privacy-sensitive context-aware applications.