Big Data-Driven Responsible Edge Intelligence: Privacy, Security, Robustness, and Potential

Dear Colleagues,

With the continuing increasing volume of data, the field of big data has been confirmed as one of the most advanced areas of research, attracting growing attention from both academia and industry. It also serves as one of the key driving forces of the information era. Meanwhile, combined with machine learning, artificial intelligence, edge computing, etc., edge intelligence has become a promising technology that provides smart services to end users at the edge of networks.

In addition, edge computing architectures have a rising requirement for being more and more autonomous. In this case, the intelligent edge servers may need to communicate with each other without the coordination of a trusted cloud center. To enable trust within this trustless scenario, blockchains have been found to be one of the most powerful tools. Consequently, big data-driven decentralized edge intelligence is a necessity to improve existing architectures.

However, the data are vast in volume and diverse in class, while being transmitted among intelligent edge servers and devices all the time. This poses further privacy challenges for data collection, data transmission, and data processing. What is worse, leading attacks such as poisoning attacks, backdoor attacks, and membership inference attacks continue to take new forms and features, making them more difficult to defeat. Therefore, in this Special Issue, we welcome the submission of research on relevant topics including, but not limited to:

• Responsible edge intelligent frameworks with big data processing abilities;
• Privacy protection of big data at the edge of networks;
• Trade-off between privacy and data utility when a blockchain is deployed as the underlying architecture, considering its transparency;
• Privacy-preserving edge intelligence;
• Privacy-preserving swarm intelligence models built upon big data;
• Security issues for big data-driven decentralized edge intelligence, especially the leading attacks such as poisoning attacks, backdoor attacks, and membership inference attacks;
• Performance improvement of edge intelligence leveraging big data;
• Other potential of responsible edge intelligence. 

Dr. Youyang Qu
Dr. Jianghua Liu
Guest Editors

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• edge computing
• big data
• machine learning
• artificial intelligence
• security issues
• data transmission