A Personalized Privacy Protection Framework for Mobile Crowdsensing in IIoT

With the rapid digitalization of various industries, mobile crowdsensing (MCS), an intelligent data collection and processing paradigm of the industrial Internet of Things, has provided a promising opportunity to construct powerful industrial systems and provide industrial services. The existing unified privacy strategy for all sensing data results in excessive or insufficient protection and low quality of crowdsensing services (QoCS) in MCS. To tackle this issue, in this article we propose a personalized privacy protection (PERIO) framework based on game theory and data encryption. Initially, we design a personalized privacy measurement algorithm to calculate users' privacy level, which is then combined with game theory to construct a rational uploading strategy. Furthermore, we propose a privacy-preserving data aggregation scheme to ensure data confidentiality, integrity, and real-timeness. Theoretical analysis and ample simulations with real trajectory dataset indicate that the PERIO scheme is effective and makes a reasonable balance between retaining high QoCS and privacy.

I. Introduction

With the rapid digitalization of various industries, unlike the conventional manual recording of all kinds of industrial data, modern industry requires various industrial data be quickly recorded in real time by as many intelligent terminals as possible to provide accurate industrial services [1]. Mobile crowdsensing (MCS) refers to an interactive, participatory, and sensing mechanism over a wireless network formed by up to billions of Internet-based mobile sensing devices with various sensors, including GPS locators, accelerometers, environmental sensors, gyroscopes, among others [2]–[4]. Consequently, MCS is recognized as a typical intelligent data collection and processing paradigm, and has provided a promising opportunity to construct powerful industrial systems and provide industrial services, where industrial sensing tasks are distributed to individuals or groups for data collection [5], information analysis, and knowledge sharing [6]. Therefore, MCS is highly suitable for the industrial Internet of Things (IIoT) [7], [8]. MCS services utilize existing mobile devices and the IoT [9] to perform large-scale and fine-grained sensing tasks at high efficiency and low costs, and therefore, are widely used in the following innovative industrial applications: intelligent transportation systems, smart urban management, health monitoring, manufacturing systems, environmental monitoring, and social security surveillance, and many others [10], [11]. MCS renders many superiorities for the intelligent transportation system in IIoT, which mainly consists of autonomous vehicles, road-side units, traffic infrastructure, and GPS service [12]. It provides real-time traffic monitoring and navigation service through real-time road condition and vehicle condition monitoring. Combining MCS with connected vehicles [13], [14] to construct vehicular crowdsensing facilitates users with more personalized, coordinated, and safer services [15], [16]. Real-time traffic monitoring in vehicular crowdsensing authorizes the cloud service provider (CSP) to continuously collect real-time driving and vehicle information and acquire traffic conditions. To reduce the transmission latency and heavy bandwidth consumption associated with remote CSPs, fog computing [17] is employed to provide location-sensitive and latency-aware data processing in vehicular crowdsensing [18], [19].