IronM: Privacy-Preserving Reliability Estimation of Heterogeneous Data for Mobile Crowdsensing

A reliable mobile crowdsensing (MCS) application usually relies on sufficient participants and trustworthy data. However, privacy concerns reduce participants' willingness to participate in sensing tasks. The uncertainty of participant behavior and heterogeneity of sensing devices result in the unreliability of sensing data and further bring unreliable MCS services. Hence, it is crucial to estimate the reliability of sensing data and protect privacy. Unfortunately, most existing privacy-preserving data estimation solutions are designed for single-type data. In practice, however, heterogeneous sensing data are ubiquitous in data integration tasks. To this end, we propose a privacy-preserving reliability estimation solution of heterogeneous data for MCS, called IronM, which is effective for text, number, and multimedia data (e.g., image, audio, and video). Specifically, IronM first formulates the reliability assessment of text, number, and multimedia data as equality and range constraints, and then estimates the reliability of heterogeneous data through our proposed privacy-preserving hybrid constraints assessment mechanism. Privacy analysis demonstrates that IronM can not only evaluate the reliability of heterogeneous data but also protect data confidentiality. The experimental results in real-world datasets show the effectiveness and efficiency of IronM.

I. Introduction

Mobile crowdsensing (MCS), as a critical component of the Internet of Things (IoT) [1], takes advantage of sensors (e.g., GPS, camera, and microphone) embedded in mobile smart devices (e.g., mobile phone) of individuals to collect sensing data and crowd wisdom to perform complex sensing tasks [2], [3], such as indoor localization [4], object tracking [5], event detection [6], smart city management [7], and environmental monitoring [8]. Besides, many commercial MCS platforms have been developed like EasyShift, Fieldagent, and SmartRoadSense. In practical data integration tasks of MCS, heterogeneous sensing data are widespread. For example, one real sensing task of EasyShift looks for several frozen breakfast products in the frozen food section of a given store, where required data include not only numerical GPS data but also images of the breakfast products and the text descriptions of the frozen food.