Submission Deadline: 31 December 2025 View: 314 Submit to Special Issue
Prof. Dr. Yongfeng Huang
Email: yfhuang@tsinghua.edu.cn
Affiliation: Department of Electronic Enginecring, Tsinghua University, Beijing 100084, China
Research Interests: information security theory, open source intelligence technology, internet security
Prof. Dr. Hongxin Hu
Email: hongxinh@buffalo.edu
Affiliation: Department of Computer Science and Engineering, University at Buffalo, SUNY, New York 14260-1660, USA
Research Interests: generative AI security, AI for social good
Dr. Sadaqat ur Rehman
Email: s.rehman15@salford.ac.uk
Affiliation: Department of Computer Science and Software Engineering, University of Salford, Manchester, M54BR, United Kingdom
Research Interests: machine learning, deep learning, data engineering
Dr. Yamin Li
Email: yamin.li@hubu.edu.cn
Affiliation: School of Computer Science, Hubei University, Wuhan 430062, China
Research Interests: steganography, information hiding, computer vision
The exponential growth of open source data (public datasets, APIs, collaborative platforms, etc.) drives innovation across scientific, industrial, and societal domains. However, its inherent accessibility introduces significant security challenges, including vulnerabilities to breaches, manipulation, unauthorized access, and sophisticated cyber- attacks. Intelligent computational modeling offers powerful approaches to understand, simulate, and secure these complex, dynamic data ecosystems. Research in this area is critically important to ensure the integrity, confidentiality, availability, and trustworthiness of open data, which is foundational for reliable applications in areas like healthcare informatics, smart cities, public policy, scientific discovery, and AI development.
This special issue aims to showcase the latest research advancements and cutting-edge solutions at the intersection of Intelligent Computational Modeling and Security for Open Source Data. We welcome contributions that leverage advanced computational techniques to model security threats, vulnerabilities, and defense mechanisms specifically tailored for open data environments. Both theoretical frameworks with rigorous foundations and applied studies demonstrating novel security solutions on real-world open data are encouraged.
Potential topics include, but are not limited to:
- Threat Modeling & Vulnerability Analysis for Open Source Data Ecosystems
- Adversarial Machine Learning (Attack & Defense) for Public Datasets & Models
- Formal Verification and Security Protocol Design for Open Data Platforms
- Privacy-Preserving Computation
- Trust & Reputation Systems
- Anomaly Detection and Intrusion Prevention
- Graph-Based Security Analytics
- Simulation and Predictive Modeling
- Scalable Security Architectures & Frameworks
- Watermarking and Fingerprinting Models
- Blockchain and Distributed Ledger Technologies
- Security of Open Source AI Models and LLMs
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