Data Verification and Privacy-respecting User Remuneration in Mobile Crowd Sensing

The broad capabilities of current mobile devices have paved the way for Mobile Crowd Sensing (MCS) applications. The success of this emerging paradigm strongly depends on the quality of received data which, in turn, is contingent to mass user participation; the broader the participation, the more useful these systems become. This can be achieved if users are gratified for their contributions while being provided with strong guarantees for the security and the privacy of their sensitive information. But this very openness is a double-edge sword: any of the participants can be adversarial and pollute the collected data in an attempt to degrade the MCS system output and, overall, its usefulness. Filtering out faulty reports is challenging, with practically no prior knowledge on the participants trustworthiness, dynamically changing phenomena, and possibly large numbers of compromised devices. This work presents a holistic framework that can assess user-submitted data and sift malicious contributions while offering adequate incentives to motivate users to submit better quality data. With a rigorous assessment of our system’s security and privacy protection complemented by a detailed experimental evaluation, we demonstrate its accuracy, practicality and scalability. Overall, our framework is a comprehensive solution that significantly extends the state-of-the-art and can catalyze the deployment of MCS applications.