Calls for papers

 

International Journal of Computational Science and Engineering

 

Special Issue on: "Security and Privacy Issues in Multi-Source Information Fusion"

Guest Editors:
Prof. Di Wu, Guangzhou University, China
Prof. Yuling Chen, Guizhou University, China
Prof. Jun Cai, Guangdong Polytechnical Normal University, China

In this era of information explosion, people are frequently being inundated by big data from various industrial applications. The most fundamental challenge for big data applications is to extract useful information or knowledge for future actions. However, raw data captured from multiple environments are complex, heterogeneous, and imperfect, making the information or knowledge extraction from them difficult. Multi-source information fusion (MSIF) is a prevalent way to deal with this issue. It merges data to obtain more informative, consistent, and accurate information than their raw format. However, MSIF is bound to cause potential security and privacy issues, which arouses public as well as government concerns. For example, MSIF commonly requires data transfer among different receivers, which has the potential risk of data leakage. Hence, the security and privacy issues have attracted extensive attention in MSIF in recent years.

This special issue aims to explore the latest up-to-date theory, methods, and applications regarding security and privacy issues of MSIF, which offers a concentrative venue for researchers to make the rapid exchange of ideas and original research findings.

The scope for this special issue will be the related studies of security and privacy issues in MSIF. Submissions may represent any theory, methods, and applications, such as federal learning-based MSIF, transfer learning-based MSIF, reliable data collection, encrypted computing, etc. In particular, new interdisciplinary approaches, open-source tools, and open-source datasets are especially welcome.

Subject Coverage
Suitable topics include, but are not limited, to the following:

• Federal learning-based MSIF
• Transfer learning-based MSIF
• Risk evaluation in MSIF
• Data security and privacy in deep learning-based MSIF
• Reliable data collection
• Encrypted computing-based MSIF
• Block chain-based MSIF
• Verifiable data fusion
• Data security and privacy in representation learning-based MSIF

Notes for Prospective Authors

Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. (N.B. Conference papers may only be submitted if the paper has been completely re-written and if appropriate written permissions have been obtained from any copyright holders of the original paper).

All papers are refereed through a peer review process.

All papers must be submitted online. To submit a paper, please read our Submitting articles page.

Important Dates

Manuscripts due by: 30 October, 2022

Notification to authors: 31 January, 2023

Final versions due by: 30 April, 2023