SecDR: Enabling Secure, Efficient, and Accurate Data Recovery for Mobile Crowdsensing

Mobile crowdsensing (MCS) has rapidly emerged as a popular paradigm for sensory data collection and benefited various location-based services and applications like road monitoring, smart transportation, and environmental monitoring. In practice, there often exist data-missing regions in the target sensing area, due to factors like limited budget, large area size, and scarcity of participants. This poses a demand for data recovery, which is commonly done based on the compressive sensing (CS) technique. However, CS-based data recovery requires access to sensory data tagged with locations, raising critical concerns on participants’ location privacy. While a plethora of location privacy techniques exist, most of them breach the data correlation inherently required by CS-based data recovery. Meanwhile, existing works mostly focus on protecting locations and overlook sensory data which may also indirectly lead to location leakages. In this paper, we propose SecDR, a new system design supporting secure, efficient, and accurate data recovery for location-based MCS applications. SecDR protects both locations and sensory data, and is built from a delicate synergy of CS-based data recovery and lightweight cryptography techniques. Extensive evaluations demonstrate that SecDR achieves promising performance and, even with stronger security guarantees, outperforms the state-of-the-art, with accuracy close to the plaintext domain.

I. Introduction

With the proliferation of various kinds of sensor-rich mobile devices (such as smartphones, wearable devices, and intelligent vehicles), mobile crowdsensing (MCS) [1], [2], [3] has rapidly emerged as a widely popular paradigm for sensory data collection regarding various kinds of objects in practice. Among others, location-based MCS has greatly benefited many real-world applications, such as road monitoring, smart transportation, and environmental monitoring [1], [4], [5], [6], [7]. For example, MCS allows harnessing of mobile devices to collect measurements in a target area for environment-related objects such as air quality, temperature, and noise pollution [5], [6], [7], [8]. In practical deployment of such location-based MCS applications, however, it is quite common that there exist in the target area blank regions whose sensory data are missing, due to various factors like the budget limit of the MCS application campaigners (commonly referred to as requesters), the large area size, or the scarcity of participants in the target area [6], [7], [9].