Advanced Techniques in Computing and Security, 2nd Edition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 June 2025 | Viewed by 4112

Special Issue Editors


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Guest Editor
School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200000, China
Interests: big data and AI; Blockchain; edge computing; networking and security; OS; information system architecture
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Guest Editor
VTT Technical Research Centre of Finland, FI-90571 Oulu, Finland
Interests: radio resource management; heterogeneous wireless networks; game theory and machine learning in 5G networks and beyond
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Guest Editor
School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou 450000, China
Interests: distributed computing; big data and artificial intelligence; network; information security
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou 450000, China
Interests: computer vision; intelligent computing; artificial intelligence; high-performance computing
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School of Software, Zhengzhou University, Zhengzhou 450000, China
Interests: multimedia transmission; wireless live streaming; edge computing; multimedia mining; surveillance video processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

After many years of development, techniques in computing and security have caused profound societal and financial effects. With the amount of data rapidly increasing, traditional methods can no longer guarantee efficiency. Providing strong and effective methods to ensure the safety, efficiency, and security of design and implementation is becoming urgent, both in the academy and industry. With the emergence of many applications, it is challenging for the current method to deal with such data effectively and safely. In particular, it is urgent to explore and exploit new technologies to collect, process, analyze, and apply such big data. Moreover, there are still many open problems in this area that need to be studied more deeply. Therefore, research focusing on advanced techniques in computing and security can establish countless potential improvements.

The objective of this Special Issue is to attract the latest research results dedicated to computing and security. This Special Issue will bring leading researchers and developers from both academia and industry together, presenting their novel research on intelligent computing and cyber security. The submitted papers will be peer-reviewed and will be selected based on the quality and relevance to the main themes of this Special Issue.

Potential topics include, but are not limited to, the following:

  • Interconnection networks;
  • Sensor, wireless, and RFID systems;
  • Network-on-chip architectures;
  • Resource allocation and management;
  • Distributed computing;
  • Network routing and traffic control;
  • Operating systems for parallel/distributed systems;
  • Software engineering for parallel/distributed systems;
  • Generalized functional safety;
  • Cloud security;
  • Intrusion detection;
  • Data provenance; 
  • Cyberscience and engineering.

Prof. Dr. Jie Li
Dr. Xianfu Chen
Prof. Dr. Lei Shi
Dr. Yangjie Cao
Dr. Bo Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computing
  • machine learning
  • sensing
  • networking
  • distributed systems
  • functional safety
  • cyber security

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Related Special Issue

Published Papers (2 papers)

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Research

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18 pages, 936 KiB  
Article
BPA: A Novel Blockchain-Based Privacy-Preserving Authentication Scheme for the Internet of Vehicles
by Jie Li, Yuanyuan Lin, Yibing Li, Yan Zhuang and Yangjie Cao
Electronics 2024, 13(10), 1901; https://doi.org/10.3390/electronics13101901 - 13 May 2024
Cited by 2 | Viewed by 1824
Abstract
The Internet of Vehicles (IoV) connects an isolated individual on the road to share information, which can improve traffic efficiency. However, the promotion of information sharing brings the critical security issues of identity authentication, followed by privacy protection issues in the authentication process [...] Read more.
The Internet of Vehicles (IoV) connects an isolated individual on the road to share information, which can improve traffic efficiency. However, the promotion of information sharing brings the critical security issues of identity authentication, followed by privacy protection issues in the authentication process in the IoV. In this study, we designed a blockchain-based conditional privacy-preserving authentication scheme for the IoV (BPA). Our scheme implements zero-knowledge proof (ZKP) to verify the identities of vehicles, which moves the authentication process down to the Roadside Units (RSUs) and achieves decentralized authentication at the edge nodes. Moreover, blockchain technology is utilized to synchronize a consistent ledger across all RSUs for recording and disseminating vehicle authentication states, which enhances the overall authentication process efficiency. We provide a theoretical analysis asserting that the BPA ensures enhanced security and effectively protects the privacy of all participating vehicles. Experimental evaluations confirm that our scheme outperforms existing solutions in terms of the computational and communication overhead. Full article
(This article belongs to the Special Issue Advanced Techniques in Computing and Security, 2nd Edition)
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Review

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35 pages, 3980 KiB  
Review
Addressing Bias and Fairness Using Fair Federated Learning: A Synthetic Review
by Dohyoung Kim, Hyekyung Woo and Youngho Lee
Electronics 2024, 13(23), 4664; https://doi.org/10.3390/electronics13234664 - 26 Nov 2024
Cited by 2 | Viewed by 1890
Abstract
The rapid increase in data volume and variety within the field of machine learning necessitates ethical data utilization and adherence to strict privacy protection standards. Fair federated learning (FFL) has emerged as a pivotal solution for ensuring fairness and privacy protection within distributed [...] Read more.
The rapid increase in data volume and variety within the field of machine learning necessitates ethical data utilization and adherence to strict privacy protection standards. Fair federated learning (FFL) has emerged as a pivotal solution for ensuring fairness and privacy protection within distributed learning environments. FFL not only enhances privacy safeguards but also addresses inherent limitations of existing federated learning (FL) systems by fostering equitable model training across diverse participant groups, mitigating the exclusion of individual users or minorities, and improving overall model fairness. This study examines the causes of bias and fairness within existing FL systems and categorizes solutions according to data partitioning strategies, privacy mechanisms, applicable machine learning models, communication architectures, and technologies designed to manage heterogeneity. To mitigate bias, enhance fairness, and strengthen privacy protections in FL, this study also explores fairness evaluation metrics, relevant applications, and associated challenges of FFL. Addressing bias, fairness, and privacy concerns across all mechanisms serves as a valuable resource for practitioners aiming to develop efficient FL solutions. Full article
(This article belongs to the Special Issue Advanced Techniques in Computing and Security, 2nd Edition)
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