Security and Communication Networks

Security, Privacy and Trust Challenges in Mobile Crowdsensing


Publishing date
01 Jul 2021
Status
Closed
Submission deadline
26 Feb 2021

Lead Editor

1Fuzhou University, Singapore

2Luleå University of Technology, Luleå, Sweden

3City University of Hong Kong, Hong Kong

This issue is now closed for submissions.
More articles will be published in the near future.

Security, Privacy and Trust Challenges in Mobile Crowdsensing

This issue is now closed for submissions.
More articles will be published in the near future.

Description

Mobile crowdsensing systems (MCS) rely on contributions from mobile devices of a large number of participants or a crowd, where a large group of individuals having mobile devices capable of sensing and computing collectively share data and extract information to measure, map, analyze, estimate, or infer (predict) any processes of common interest. It is advantageous in low deployment cost and vast geographic coverage and has found numerous applications in diverse domains, including transportation, environment monitoring, smart cities, pervasive healthcare, and so on.

However, MCS systems often face the challenge of security, privacy, and trust issues. Motion sensors such as accelerometers and gyroscopes embedded in smartphones play an important role in monitoring our real-world surroundings, which is related to numerous privacy invasion attacks, such as private information leaks related to human behaviours, physical features, and location information. Although users carrying mobile smart devices are willing to participate in the mobile sensing process, they may not disclose their private information like location, voice, and operating record. Additionally, the MCS can easily suffer from side-channel attacks, where physical information leakage during the operation of basic sensors is exploited to deduce the confidential data in mobile crowdsensing applications. Most of the security and privacy issues can be solved with some traditional cryptography methods; however, the heavyweight cryptosystem still cannot be performed on the billions of resource-constrained smart mobile or Internet of Things devices, which restrict the applications in MCS.

The aim of this Special Issue is to solicit original research and review articles to gather recent advanced security, privacy, and trust techniques relevant to the convergence of mobile crowdsensing. In particular, experimental approaches with publicly released code, tools, and data are welcomed.

Potential topics include but are not limited to the following:

  • Authentication in mobile crowdsensing
  • Lightweight data protection scheme for MCS
  • Secure model protection method for AI in MCS
  • Hardware security and privacy issues for IoT and mobile devices
  • Secure data integrity and validation techniques for MCS
  • Privacy computation and processing protocols for crowdsensing
  • Secure M2M communication for distributed mobile devices
  • Formal security model for cryptographic protocols for MCS
  • Future and secure IoT-based data collection methods in MCS
  • Trust and reputation system in MCS
  • Secure data mining and machine learning in MCS
  • Virtualization, security, and management for MCS
  • Side-channel attacks and defence methods for MCS
  • Blockchain and smart contracts for MCS
  • New privacy challenges in MCS

Articles

  • Special Issue
  • - Volume 2021
  • - Article ID 5519558
  • - Research Article

Achieving Lightweight Privacy-Preserving Image Sharing and Illegal Distributor Detection in Social IoT

Tianpeng Deng | Xuan Li | ... | Jie Lin
  • Special Issue
  • - Volume 2021
  • - Article ID 5598574
  • - Research Article

Privacy Enhancement on Unilateral Bluetooth Authentication Protocol for Mobile Crowdsensing

Da-Zhi Sun | Ji-Dong Zhong
  • Special Issue
  • - Volume 2021
  • - Article ID 5514137
  • - Research Article

PvCT: A Publicly Verifiable Contact Tracing Algorithm in Cloud Computing

Yixiao Zhu | Wenjie Ma | ... | Jianting Ning
  • Special Issue
  • - Volume 2021
  • - Article ID 6662135
  • - Research Article

Audit Outsourced Data in Internet of Things

Gaopan Hou | Jianfeng Ma | ... | Chen Liang
  • Special Issue
  • - Volume 2021
  • - Article ID 6654539
  • - Research Article

Verifiable Location-Encrypted Spatial Aggregation Computing for Mobile Crowd Sensing

Kun Niu | Changgen Peng | ... | Yi Xu
  • Special Issue
  • - Volume 2021
  • - Article ID 5574415
  • - Research Article

Differential Privacy Location Protection Scheme Based on Hilbert Curve

Jie Wang | Feng Wang | Hongtao Li
  • Special Issue
  • - Volume 2021
  • - Article ID 6646445
  • - Research Article

Efficient Hierarchical and Time-Sensitive Data Sharing with User Revocation in Mobile Crowdsensing

Jiawei Zhang | Jianfeng Ma | ... | Qi Jiang
  • Special Issue
  • - Volume 2021
  • - Article ID 6613392
  • - Research Article

A New Password- and Position-Based Authenticated Key Exchange

Jia Fan | Lanfei Qiao | ... | Lin Tang
  • Special Issue
  • - Volume 2021
  • - Article ID 6682456
  • - Research Article

Fine-Grained Task Access Control System for Mobile Crowdsensing

Jingwei Wang | Xinchun Yin | Jianting Ning
  • Special Issue
  • - Volume 2021
  • - Article ID 6679157
  • - Research Article

Towards Time-Sensitive and Verifiable Data Aggregation for Mobile Crowdsensing

Tao Zhang | Xiongfei Song | ... | Qi Li
Security and Communication Networks
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