Smartphone technology in urban communities has enabled mobile users to utilize context-aware services on their devices. Service providers take advantage of this dynamic and ever-growing technology landscape by proposing innovative context-dependent services for mobile subscribers. Location-based Services (LBS), for example, are used by millions of mobile subscribers every day to obtain location-specific information. In this abstract, we propose privacy-preserving algorithms for determining an optimal meeting location for a group of users. We perform a thorough privacy evaluation by formally quantifying privacy-loss of the proposed approaches. In order to study the performance of our algorithms in a real deployment, we implement and test their execution efficiency on Nokia smart phones. By means of a targeted user-study, we attempt to get an insight into the privacy-awareness of users in location-based services and the usability of the proposed solutions.
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