Data Privacy and Cybersecurity in Mobile Crowdsensing

Dear Colleagues,

As a novel data sensing paradigm that integrates mobile sensing and crowdsourcing, mobile crowdsensing (MCS) has attracted extensive attention from both academia and industry. Centered on users with various kinds of sensors, MCS can leverage human–sensor cooperation and human–human cooperation to solve many challenging problems. Compared with the traditional wireless sensor networks with fixed sensors, MCS has the advantages of high sensing efficiency, long life cycle, and low deployment cost. MCS has been widely applied in various fields, such as environment monitoring, transportation management, and medical health.

Despite its numerous merits, MCS comes with its own set of challenges, especially in terms of security and privacy protection. Malicious behaviors such as entity impersonation, unauthorized access, and cross-network attacks could be fatal to MCS. Constrained by the limited computation and communication overhead of mobile devices, conventional cryptographic-primitives-based approaches cannot be directly exploited. In addition, in MCS, the users or devices are moving all the time, making communication and cross-domain access difficult.

The objective of this Special Issue is to explore recent advances that address fundamental and practical challenges related to security and privacy in MCS. High-quality original research and review articles in this area are expected. Potential topics include, but are not limited to, the following:

• Secure task allocation mechanisms in MCS;
• Secure data sharing mechanisms in MCS;
• Secure data analysis mechanisms in MCS;
• User trust and incentive mechanisms in MCS;
• Access control in MCS;
• Privacyenhanced technologies for MCS;
• Cyber attacks in MCS;
• Design and deployment of novel and secure MCS systems.

Dr. Chuan Zhang
Dr. Tong Wu
Dr. Weiting Zhang
Guest Editors

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