The Accuracy-Privacy Tradeoff of Mobile Crowdsensing

TL;DR

This paper explores the balance between privacy and accuracy in mobile crowdsensing, proposing mechanisms to incentivize truthful data sharing while protecting user privacy and enabling cooperation among users.

Contribution

It introduces a truthful mechanism for privacy-aware data collection and a coalition strategy for enhanced privacy and shared incentives in mobile crowdsensing.

Findings

Proposed a mechanism incentivizing truthful data contribution.

Developed a coalition strategy for privacy and cooperation.

Outlined open research directions in the field.

Abstract

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.