Data Privacy Protection in the Internet of Things

Dear Colleagues,

Nowadays, the Internet of Things (IoT) allows regular users to upload sensing data collected via their smart devices, which constitutes an indispensable component of IoT data acquisition. These users, while working as data contributors, also form a community where they are comprehensively collaborative during the procedure of data collection, communication, analysis, and services. It is believed that the IoT is a promising paradigm that brings seamless and flexible coverage for our surrounding environment via the extensive and voluntary involvement of crowds. However, this new trend also brings significant threats to data security and privacy. Sensing contents are more likely to be hijacked within the open and vulnerable system, and the sensitive information of users could be recovered. For example, trajectories recorded by mobile phones may indicate the daily status of users like health, incoming, sex orientation, etc. These threats are made even worse by the fact that users of IoTs usually continuously and unconsciously contribute sensing data to systems via devices and apps running in the background of operating systems. Moreover, the IoT also suffers high dynamics and heterogeneity due to the diverse behaviors and preference of participants. Both facts aggravate the difficulties for security and privacy preservation, which have already thwarted the current development of  IoTs.

In recent years, multiple techniques have flourished which enhance security and privacy preservation capabilities in diverse distributed systems. Some noticeable terms include federated learning, privacy-aware computing, blockchains, AI-powered security, Zero Trust, etc. These techniques have both broadened the scope of system security and also strengthened the level of privacy preservation, as continuous and heterogeneous behaviors of regular and malicious participants are both handled. 

We argue that there should also be an in-depth and comprehensive consideration on integrating these cutting-edge techniques with the design of IoTs, which includes but is not limited to novel theories, frameworks, techniques, and applications. Therefore, this Special Issue will be a virtual research forum for the sharing of insightful ideas towards secure and privacy-aware computing in IoTs.

The topics of interest include, but are not limited to:

• Fundamental theories on security and privacy preservation in IoTs;
• Privacy-aware computing for data processing in IoTs;
• Federated learning for data processing in IoTs;
• Design and implementation of blockchain techniques for IoTs;
• Design and implementation of Zero Trust approaches for IoTs;
• Design of authentication mechanisms for dynamic devices in IoTs;
• Design of lightweight encryption and decryption methods for IoTs;
• Reinforcement learning methods for data processing in IoTs;
• Network protocols supporting privacy-aware computing in IoTs;
• Demos, systems and applications for security- and privacy-aware computing in IoTs.

Dr. Xu Zheng
Dr. Zhuojun Duan
Prof. Dr. Yingjie Wang
Guest Editors

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