Call for papers: Special issue on privacy-preserving data mining for artificial intelligence of things

Artificial Intelligence of Things (AIoT) is experiencing unimaginable fast booming with the popularization of end devices and advanced machine learning and data processing techniques. An increasing volume of data is being collected every single second to enable Artificial Intelligence (AI) on the Internet of Things (IoT). The explosion of data brings significant benefits to various intelligent industries to provide predictive services and research institutes to advance human knowledge in data-intensive fields. To make the best use of the collected data, various data mining techniques have been deployed to extract data patterns. In classic scenarios, the data collected from IoT devices is directly sent to cloud servers for processing using diverse methods such as training machine learning models. However, the network between cloud servers and massive end devices may not be stable due to irregular bursts of traffic, weather, etc. Therefore, autonomous data mining that is self-organized by a group of local devices to maintain ongoing and robust AI services plays a growing important role for critical IoT infrastructures. Privacy issues become more concerning in this scenario. The data transmitted via autonomous networks are publicly accessible by all internal participants, which increases the risk of exposure. Besides, data mining techniques may reveal sensitive information from the collected data. Various attacks, such as inference attacks, are emerging and evolving to breach sensitive data due to its great financial benefits. Motivated by this, it is essential to devise novel privacy-preserving autonomous data mining solutions for AIoT. In this Special Issue, we aim to gather state-of-art advances in privacy-preserving data mining and autonomous data processing solutions for AIoT. Topics include, but are not limited to, the following: • Privacy-preserving federated learning for AIoT • Differentially private machine learning for AIoT • Personalized privacy-preserving data mining • Decentralized machine learning paradigms for autonomous data mining using blockchain • AI-enhanced edge data mining for AIoT • AI and blockchain empowered privacy-preserving big data analytics for AIoT • Anomaly detection and inference attack defense for AIoT • Privacy protection measurement metrics • Zero trust architectures for privacy protection management • Privacy protection data mining and analysis via blockchain-enabled digital twin.