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Electronics
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Digital Security and Privacy Protection: Trends and Applications, 3nd Edition
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Digital Security and Privacy Protection: Trends and Applications, 3nd Edition

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

Deadline for manuscript submissions: 31 October 2026 | Viewed by 1748

Special Issue Editor

Prof. Dr. Cheonshik Kim

E-Mail Website
Guest Editor
Department of Computer Science, Sejong University, Seoul 05006, Republic of Korea
Interests: application of DSP; information theory; data hiding
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Since digital data, such as personal information, corporate business secrets, and important national facilities, are stored and utilized in the institution's server or cloud server, they are protected and managed by a high-level information protection program. In recent decades, informatization and digitization have fundamentally changed the way we work and have exposed security issues for individuals and businesses. With the technological development of new technologies such as IoT and AI, interest has been taken in the increase and utilization of data. In addition, new methods of acquiring data have been introduced. Data analysis is being studied for valuable uses of data, and it is being actively studied in academia, companies, and governments. However, since sensitive digital data can be used as ransomware, research is also needed to solve this problem.

This Special Issue aims to advance the state of the art by gathering original research in the field of software-intensive systems, fundamental connections between the theory of information protection and extensive research on security issues for digital assets and various IT systems and devices. There is no limit to the broad content of various computer engineering topics outside the subject of this Special Issue.

Prof. Dr. Cheonshik Kim
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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

  • cybersecurity
  • privacy protection
  • information security
  • computing security
  • blockchain
  • big data analysis and applications
  • social network information
  • digital forensics
  • data hiding
  • watermarking

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Related Special Issue

Published Papers (2 papers)

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41 pages, 1538 KB  
Article
SplitML: A Unified Privacy-Preserving Architecture for Federated Split-Learning in Heterogeneous Environments
by Devharsh Trivedi, Aymen Boudguiga, Nesrine Kaaniche and Nikos Triandopoulos
Electronics 2026, 15(2), 267; https://doi.org/10.3390/electronics15020267 - 7 Jan 2026
Cited by 1 | Viewed by 451
Abstract
While Federated Learning (FL) and Split Learning (SL) aim to uphold data confidentiality by localized training, they remain susceptible to adversarial threats such as model poisoning and sophisticated inference attacks. To mitigate these vulnerabilities, we propose SplitML, a secure and privacy-preserving framework [...] Read more.
While Federated Learning (FL) and Split Learning (SL) aim to uphold data confidentiality by localized training, they remain susceptible to adversarial threats such as model poisoning and sophisticated inference attacks. To mitigate these vulnerabilities, we propose SplitML, a secure and privacy-preserving framework for Federated Split Learning (FSL). By integrating IND−CPAD secure Fully Homomorphic Encryption (FHE) with Differential Privacy (DP), SplitML establishes a defense-in-depth strategy that minimizes information leakage and thwarts reconstructive inference attempts. The framework accommodates heterogeneous model architectures by allowing clients to collaboratively train only the common top layers while keeping their bottom layers exclusive to each participant. This partitioning strategy ensures that the layers closest to the sensitive input data are never exposed to the centralized server. During the training phase, participants utilize multi-key CKKS FHE to facilitate secure weight aggregation, which ensures that no single entity can access individual updates in plaintext. For collaborative inference, clients exchange activations protected by single-key CKKS FHE to achieve a consensus derived from Total Labels (TL) or Total Predictions (TP). This consensus mechanism enhances decision reliability by aggregating decentralized insights while obfuscating soft-label confidence scores that could be exploited by attackers. Our empirical evaluation demonstrates that SplitML provides substantial defense against Membership Inference (MI) attacks, reduces temporal training costs compared to standard encrypted FL, and improves inference precision via its consensus mechanism, all while maintaining a negligible impact on federation overhead. Full article
(This article belongs to the Special Issue Digital Security and Privacy Protection: Trends and Applications, 3nd Edition)
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19 pages, 2342 KB  
Article
Person Re-Identification Enhanced by Super-Resolution Technology
by Yue Liu, Zewen Li, Lu Leng and Cheonshik Kim
Electronics 2025, 14(23), 4647; https://doi.org/10.3390/electronics14234647 - 26 Nov 2025
Viewed by 1080
Abstract
With rising demand for cross-camera person re-identification (ReID) in smart cities, low-resolution (LR) images severely hinder practical ReID performance due to detail loss and weakened identity features. This paper proposes two solutions to address this bottleneck: (1) super-resolution (SR) techniques, including hybrid attention [...] Read more.
With rising demand for cross-camera person re-identification (ReID) in smart cities, low-resolution (LR) images severely hinder practical ReID performance due to detail loss and weakened identity features. This paper proposes two solutions to address this bottleneck: (1) super-resolution (SR) techniques, including hybrid attention transformer (HAT), pixel-level and semantic-level adjustable SR (PiSA-SR), and omni aggregation networks for lightweight image SR (Omni-SR), are used to enhance image visual quality, and the enhanced images are applied to three ReID methods, including semantically controllable self-supervised learning framework-REID (SOLIDER-REID), light-REID, and relation-aware global attention (RGA), for performance assessment. (2) An end-to-end framework integrating HAT and SOLIDER-REID is designed, in which HAT enhances LR images via multi-scale attention to restore discriminative details, while SOLIDER-REID’s semantic controller suppresses background noise to focus on the pedestrian regions. Extensive experiments on the Market-1501 dataset show that the first solution slightly improves ReID accuracy, e.g., PiSA-SR + SOLIDER-REID achieves 92.0% mAP, 0.4% higher than SOLIDER-REID alone, while slightly sacrificing speed. The second solution significantly boosts LR ReID performance at the cost of a certain increase in time. For LR images, even 32 × 32 images, HAT-SOLIDER achieves 59.8% mAP and 80.4% Rank-1, 18.5% higher in mAP and 19.2% higher in Rank-1 than SOLIDER-REID alone. This work provides effective solutions for LR-induced performance degradation in cross-camera ReID. Full article
(This article belongs to the Special Issue Digital Security and Privacy Protection: Trends and Applications, 3nd Edition)
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