With the development of the Internet of Things (IoT) and the development of wearable sensors, a participatory sensor network is formulated through daily mobile devices, in which a large number of sensors act as participants to perform sensing tasks, such as environmental monitoring, behaviour monitoring, traffic monitoring, and other tasks. In IoT, while vast amounts of data are perceived, collected, analyzed, and then uploaded by participants, the privacy concerns on collecting data have received wide attention and become a research hotspot.

The rapid development of the fifth-generation (5G) cellular technology has enabled a new way to collect data and raises new privacy issues to IoT. Most participatory sensing privacy protection methods only protect participants' private information locally (i.e., on a daily mobile device) by some means, but do not take into account the protection of communication (i.e., the process of network transmission). Attackers may also monitor and steal private information from participants on unsecured channels, increasing the risk of revealing participants' private data. Therefore, how to combine traditional cryptography methods with participatory sensing to realize participants' privacy protection in a diversified service environment has become a critical problem in IoT. Another common key challenge for participatory sensor networks is how to inspire mobile devices to collect data. Unfortunately, it is still an open problem to consider privacy protection and incentive problem for data in IoT simultaneously. More importantly, Artificial intelligence (AI), machine learning, big data analytics, etc., have paved the path for a new era of competition where data collected from IoT is considered as a living and evolving asset that can unlock enormous new opportunities.

This Special Issue aims to provide a platform for researchers to present novel and effective privacy protection and incentive for AI-driven IoT. Original research and review articles are welcome.

Potential topics include but are not limited to the following:

• Privacy protection for cloud computing in IoT
• Privacy protection for edge computing in IoT
• Secure communications on data collection for IoT
• Privacy protection for IoT under resource constraints
• Task allocation with privacy protection for IoT
• Quality control with privacy protection for IoT
• Privacy protection and machine learning algorithms for IoT
• Privacy and security of sensed data for IoT
• Privacy-aware incentive mechanism
• Trust-oriented design for IoT
• Blockchain and privacy protection for distributed network
• Blockchain and trust computing for IoT
• Privacy solutions of IoT with edge and blockchain
• Security design and enhancement in edge computing and IoT
• Optimization of the utility-privacy tradeoffs for IoT
• AI for Quality-of-Service (QoS) Management in IoT