  Mobile crowdsensing has emerged as an efficient sensing paradigm which
combines the crowd intelligence and the sensing power of mobile devices,
e.g.,~mobile phones and Internet of Things (IoT) gadgets. This article
addresses the contradicting incentives of privacy preservation by crowdsensing
users and accuracy maximization and collection of true data by service
providers. We firstly define the individual contributions of crowdsensing users
based on the accuracy in data analytics achieved by the service provider from
buying their data. We then propose a truthful mechanism for achieving high
service accuracy while protecting the privacy based on the user preferences.
The users are incentivized to provide true data by being paid based on their
individual contribution to the overall service accuracy. Moreover, we propose a
coalition strategy which allows users to cooperate in providing their data
under one identity, increasing their anonymity privacy protection, and sharing
the resulting payoff. Finally, we outline important open research directions in
mobile and people-centric crowdsensing.
