SI2022-09 H-Social Big Data Privacy 2022 : Call for papers: Human-centric Social Big Data Privacy, Security and Frontier computing (HCIS Q1)
SI2022-09 H-Social Big Data Privacy 2022 : Call for papers: Human-centric Social Big Data Privacy, Security and Frontier computing (HCIS Q1)

SI2022-09 H-Social Big Data Privacy 2022 : Call for papers: Human-centric Social Big Data Privacy, Security and Frontier computing (HCIS Q1)

Event Date: August 15, 2020 - December 15, 2020
Submission Deadline: December 15, 2020
Notification of Acceptance: January 15, 2023
Camera Ready Version Due: March 15, 2023

Call for Papers

Human-centric Computing and Information Sciences (HCIS)
Korea Information Processing Society-Computer Software Research Group
(KIPS-CSWG)(ISSN: 2192-1962)

Impact Factor of 6.558, ranks 27/164 (Q1) in category "COMPUTER SCIENCES, INFORMATION SYSTEMS", which was released in the 2021 Journal Citation Reports® (Clarivate Analytics, 2022).

Call for papers: Human-centric Social Big Data Privacy, Security and Frontier computing

HCIS official website:

Nowadays, some countries, regions and local governments around the world have put the construction of “digital government” and “digital community” on the agenda, and the security governance of human-centric social big data has become a crucial part of the construction of digital government and the focus of attention of all parties. The research and governance of social big data, including government big data, industry big data and social public big data, are faced with “isolated island of information” and “data chimneys”, as well as many challenges in the governance of big data security integration, security computing and industry enterprise data asset evaluation.
The main research currently being carried out by academia and industry includes the use of AI cutting-edge technologies and new computing paradigms to integrate into human-centric social big data governance research, and the exploration of security theory for the whole domain, whole process and whole life cycle of social big data. Research the social big data security fusion architecture and homomorphic encryption technology based on federated learning, so as to realize the privacy protection and maximize the value of social big data in the fusion of sensitive data of all parties. Research the hybrid attention mechanism algorithm and anti-attack method based on deep learning and explore the relationship between the manifold of the original sample space and mobility, so as to effectively improve the endogenous security of social big data analysis algorithm. Introduce human and crowd intelligence into big data asset value evaluation to improve the effectiveness, credibility, and security of data asset evaluation of industrial enterprises. Moreover, through the construction of a social big data security comprehensive management platform for digital government and community, standardization and industrialization demonstration applications are realized, etc.
Topics of Interests
The main goal of this special issue is to collect and present the state-of-art social big data privacy, security and frontier computing research for the rapid establishment and development of both digital government and digital community. We encourage researchers and engineers from academia, industry and government to submit high-quality original research and survey articles that represent novel theories, methodologies, case studies and applications. All submitted papers will be peer-reviewed and selected based on both their quality and their relevance to the theme of this special issue. Topics of interests include, but are not limited to:
Human-centric Social Big Data Analysis, Modeling and Frontier Computing
Social Big Data Security and Computing by using Machine Intelligence and Human Crowd Intelligence
Human-centric Social Situational Meta Data Security in Online Community
Social Big Data Privacy Computing, Differential Privacy and Homomorphic Encryption
Zero Trust Authentication and Dynamic Access Control over Social Big Data
Federated Learning/Deep Learning Architecture, Model an Algorithm for Social Big Data Security
Human-centric Social Big Data Fusion and Service Computing
Social Big Data Enabled Emerging Digital Government and Community Applications
Social Big Data Security Case Studies for Digital Government and Community

Important Dates

Submission deadline : 15 December 2022
Author notification : within 4 weeks after submission
Revised manuscript due : within 2 weeks after notification
Notification of acceptance : within 2 weeks after revision submission
Tentative accepted paper publication date : within 2 months after final version
Tentative SI paper collection and its web open: 2nd Quarter, 2023 (TBA) 

Submission Guidelines

All submitted papers must be clearly written in excellent English and contain only original work. All papers must be submitted in an electronic format, e.g., PDF format (preferred) or MS Word. Manuscripts should follow the formatting of the sample manuscript and references. You can refer to the details in the submission menu
All papers and some supplementary materials should be submitted through ScholarOne Manuscripts. The authors must select "SI2022-09 H-Social Big Data Privacy". when they reach the “Article Type” step in the submission process
Guest Editors
Prof. Dr. Zhiyong Zhang, Lead Guest Editor
Director of Henan International Joint Laboratory of Cyberspace Security Applications, Henan University of Science and Technology, China
Email: [email protected]

Prof. Dr. Celestine Iwendi
School of Creative Technologies, University of Bolton, United Kingdom
Email: [email protected]

Prof. Dr. Longzhi Yang
Department of Computer and Information Sciences, University of Northumbria, United Kingdom
Email: [email protected]

Assoc. Prof. Dr. Jun Yan
Concordia Institute for Information Systems Engineering, Concordia University, Canada
Email: [email protected]

Credits and Sources

[1] SI2022-09 H-Social Big Data Privacy 2022 : Call for papers: Human-centric Social Big Data Privacy, Security and Frontier computing (HCIS Q1)

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