Optimizing Task Location Privacy in Mobile Crowdsensing Systems

The location information for tasks may expose sensitive information, which impedes the practical use of mobile crowdsensing in the industrial Internet. In this article, to our knowledge, we are the first to discuss the privacy protection of task locations and propose a codebook-based task allocation mechanism to protect it. Considering the cost of system utility caused by privacy protection technology, the tradeoff between local privacy and system utility is formalized a multiobjective optimization problem. The optimal solution is theoretically derived, and the optimal task allocation scheme is obtained. In addition, the selected allocation codebook (SAC) method is introduced to solve the problem of high computational resource consumption in the task allocation process and protect the task location privacy to some extent. The experimental results show that the SAC method sacrifices system utility but improves the privacy protection for task locations by 60% on average.

I. Introduction

With the rapid development of Internet technology, mobile devices are becoming increasingly popular and equipped with powerful embedded sensors (e.g., compasses, global positioning systems, thermometers, microphones, and cameras) [1]. These mobile devices with wireless sensing abilities can be used to monitor a wide variety of human activities and environments, thus creating a new Internet of Things (IoT) sensing paradigm called mobile crowdsensing (MCS). Due to their low cost and comprehensive coverage, MCS systems have become common data collection tools for various industrial IoT applications and services. In terms of data collection, MCS relies on the contributions of mobile devices from a large number of participants or groups of people. Compared with traditional sensor networks, MCS networks utilize existing sensing and mobile communication infrastructures to provide unprecedented coverage of time and space. Due to the powerful perception and communication abilities of MCS systems, such systems have been widely developed and used in various applications, including traffic management [2], road surface condition monitoring [3], [4], daily lifestyle monitoring in the elderly population [5], and air pollution detection [6]. The typical system architecture of MCSs includes three parts: service platforms, requesters, and data providers (i.e., workers) [7]–[9]. The process of MCS is shown in Fig. 1, which involves the basic functions of data perception, data acquisition, and information service provision in a distributed and independent service mode.