The context toolkit: aiding the development of context-enabled applications
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
A Privacy Awareness System for Ubiquitous Computing Environments
UbiComp '02 Proceedings of the 4th international conference on Ubiquitous Computing
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
FPCS: A Formal Approach for Privacy-Aware Context-Based Services
COMPSAC '08 Proceedings of the 2008 32nd Annual IEEE International Computer Software and Applications Conference
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Privacy is the most often-cited criticism of context awareness in pervasive environments and may be the utmost barrier to its enduring success. Users certainly desire to be notified of potential data capture. Context-based pervasive applications have the vulnerabilities of tracking and capturing extensive portions of users' activities. Whether such data capture is an actual threat or not, users' perceptions of such possibilities may discourage them from using and adopting pervasive applications. So far in context-based pervasive applications, location data has been the main focus to make users anonymous. However in reality, anonymity depends on all the privacy sensitive data collected by the applications. Protecting anonymity with the help of an anonymizer has the susceptibility of a single point of failure. In this poster, we propose a formal model ProQuPri (Protect Anonymity and Quantify Privacy) that preserves users' anonymity without anonymizer while quantifies the amount of privacy at the time asking for services from untrustworthy service providers. Before placing a request, each user can protect his own anonymity by collaborating with his peers.