Empowering mobile crowdsourcing apps with user privacy control

Abstract

Mobile crowdsourcing is being increasingly used by industrial and research communities to build realistic datasets. By leveraging the capabilities of mobile devices, mobile crowdsourcing apps can be used to track participants’ activity and to collect insightful reports from the environment (e.g., air quality, network quality). However, most of existing crowdsourced datasets systematically tag data samples with metadata (e.g., time and location stamps), which may inevitably lead to user privacy leaks by discarding sensitive information in the wild.This article addresses this critical limitation of the state of the art by proposing a software library that empowers legacy mobile crowdsourcing apps to increase user privacy without compromising the overall quality of the crowdsourced datasets. We propose a decentralized approach, named Fougere, to convey data samples from user devices to third-party servers. By introducing an a priori data anonymization process, we show that Fougere defeats state-of-the-art location-based privacy attacks with little impact on the quality of crowdsourced datasets.