Differentially Private and Skew-Aware Spatial Decompositions for Mobile Crowdsensing.

Jong Kim,Yon Chung

doi:10.3390/s18113696

Abstract

Mobile Crowdsensing (MCS) is a paradigm for collecting large-scale sensor data by leveraging mobile devices equipped with small and low-powered sensors. MCS has recently received considerable attention from diverse fields, because it can reduce the cost incurred in the process of collecting a large amount of sensor data. However, in the task assignment process in MCS, to allocate the requested tasks efficiently, the workers need to send their specific location to the requester, which can raise serious location privacy issues. In this paper, we focus on the methods for publishing differentially a private spatial histogram to guarantee the location privacy of the workers. The private spatial histogram is a sanitized spatial index where each node represents the sub-regions and contains the noisy counts of the objects in each sub-region. With the sanitized spatial histograms, it is possible to estimate approximately the number of workers in the arbitrary area, while preserving their location privacy. However, the existing methods have given little concern to the domain size of the input dataset, leading to the low estimation accuracy. This paper proposes a partitioning technique SAGA (Skew-Aware Grid pArtitioning) based on the hotspots, which is more appropriate to adjust the domain size of the dataset. Further, to optimize the overall errors, we lay a uniform grid in each hotspot. Experimental results on four real-world datasets show that our method provides an enhanced query accuracy compared to the existing methods.

Highlights

With the rapid developments of information technology, we are using various mobile devices during daily activity
We found that the domain size and density of an input dataset are two major factors affecting the accuracy of Private Spatial Decomposition (PSD)
We explored the privacy issue associated with mobile crowdsensing

Summary

Introduction

With the rapid developments of information technology, we are using various mobile devices during daily activity. Mobile devices, equipped with several sensors, enable users to measure and monitor environmental conditions such as air pollution, temperature and humidity. A paradigm to collect large-scale sensing data through the prevalent mobile devices has recently emerged, which is referred to as Mobile Crowdsensing (MCS). With MCS, the requesters assign the set of tasks to the participating workers, and the workers move to specific locations and perform the assigned tasks. This process is performed by the MCS server, which plays a role as the intermediary [1]. Consider a scenario where each worker holds a mobile device that can monitor current air quality conditions. A requester wants to collect air quality data in a particular region and transmits the tasks of sensing air quality to the MCS server

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