Enabling Strong Privacy Preservation and Accurate Task Allocation for Mobile Crowdsensing

Mobile crowdsensing engages a crowd of individuals to use their mobile devices to cooperatively collect data about social events and phenomena for customers with common interest. It can reduce the cost on sensor deployment and improve data quality with human intelligence. To enhance data trustworthiness, it is critical for the service provider to recruit mobile users based on their personal features, e.g., mobility pattern and reputation, but it leads to the privacy leakage of mobile users. Therefore, how to resolve the contradiction between user privacy and task allocation is challenging in mobile crowdsensing. In this paper, we propose SPOON, a strong privacy-preserving mobile crowdsensing scheme supporting accurate task allocation based on geographic information and credit points of mobile users. In SPOON, the service provider enables to recruit mobile users based on their locations, and select proper sensing reports according to their trust levels without invading user privacy. By utilizing proxy re-encryption and BBS+ signature, sensing tasks are protected and reports are anonymized to prevent privacy leakage. In addition, a privacy-preserving credit management mechanism is introduced to achieve decentralized trust management and secure credit proof for mobile users. Finally, we show the security properties of SPOON and demonstrate its efficiency in terms of computation and communication.

1 Introduction

The integration of sensors and embedded computing devices triggers the emergence of mobile crowdsensing [2], in which user-centric mobile devices, e.g., smartphones, in-vehicle devices and wearable devices, are utilized to sense, collect and process data about social events and phenomena. This “sensing as a service” [3] elaborates our knowledge of the physical world by opening up a new door for data collection and sharing [4]. Due to the increasing popularity of mobile devices, mobile crowdsensing supports a broad range of sensing applications nowadays, ranging from social recommendation, such as restaurant recommendation, parking space discovery and indoor floor plan reconstrction [5], to environment monitoring, such as air quality measurement, noise level detection and dam water release warning. With human intelligence and user mobility, mobile crowdsensing can significantly improve the trustworthiness of sensing data, extend the scale of sensing applications and reduce the cost on high-quality data collection [6].