Security and Privacy in Mobile Crowdsensing: Models, Progresses, and Trends

被引：0
|
作者：
Xiong J.-B. [1 ]
Bi R.-W. [1 ]
Tian Y.-L. [2 ]
Liu X.-M. [3 ]
Ma J.-F. [4 ]
机构：
[1] College of Computer and Cyber Security, Fujian Normal University, Fuzhou
[2] College of Computer Science and Technology, Guizhou University, Guiyang
[3] College of Mathematics and Computer Science, Fuzhou University, Fuzhou
[4] Shaanxi Key Laboratory of Network and System Security, Xidian University, Xi'an
来源：
Tian, You-Liang (youliangtian@163.com) | 1949年 / Science Press卷 / 44期
基金：
中国国家自然科学基金;
关键词：
Data security; Differential privacy; Game theory model; Mobile crowdsensing; Privacy protection;
D O I：
10.11897/SP.J.1016.2021.01949
中图分类号：
学科分类号：
摘要：
Having emerged as a novel intelligent perception paradigm of the Internet of Things, mobile crowdsensing (MCS) is capable of handling large-scale and complex social sensing tasks and service applications in human society by motivating modern intelligent sensing devices to provide high-quality sensing data. With the exploding growth of smart devices, MCS has made a rapid progress in recent years, and has greatly enriched various applications and services in smart city, such as intelligent transportation, connected healthcare, smart energy, ambient monitoring, etc. An MCS system is mainly composed of sensing users, sensing platforms and service providers. In the duration of a sensing task, the relevant sensing data goes through three stages: data sensing, data uploading and data trading. Each stage is faced with a variety of risks that could endanger sensing users' privacy and sensing data security. The existing works mainly discuss the security and privacy of MCS from one stage or one particular security problem, lacking an overall and systematic perspective. Without comprehensively addressing the security and privacy issues, the continuous developments of MCS could be hindered. This paper introduces a system model and real application scenarios of MCS, followed by the main security research methods. Taking the lifecycle of sensing data participating in the sensing task as the axis, we discuss the security and privacy threats in all three stages of the sensing data lifecycle. Targeting these threats, we elaborately describe the existing security and privacy protection solutions from data security, location privacy and identity privacy. We give the further developing trends and research directions of MCS to conclude the paper, such as individual dynamic privacy measurement, adaptive privacy-preserving framework, blockchain-based secure key management, multi-meta privacy protection and integrated privacy computing, etc. © 2021, Science Press. All right reserved.
引用
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页码：1949 / 1966
页数：17
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