The Accuracy-Privacy Trade-off of Mobile Crowdsensing
Alsheikh, Mohammad Abu
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Mobile crowdsensing has emerged as an efficient sensing paradigm that combines the crowd intelligence and the sensing power of mobile devices, such as mobile phones and Internet of Things 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 first 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 privacy based on 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 that 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.