ProQuPri: Towards anonymity protection with privacy quantification for context-aware applications

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.