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Electronics
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Digital Security and Privacy Protection: Trends and Applications, 2nd Edition
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Digital Security and Privacy Protection: Trends and Applications, 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 October 2025 | Viewed by 6901

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. Informatization and digitization in recent decades 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 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

  • 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 (9 papers)

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Research

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11 pages, 817 KiB  
Article
Investigating De-Identification Methodologies in Dutch Medical Texts: A Replication Study of Deduce and Deidentify
by Pablo Mosteiro, Ruilin Wang, Floortje Scheepers and Marco Spruit
Electronics 2025, 14(8), 1636; https://doi.org/10.3390/electronics14081636 - 18 Apr 2025
Viewed by 167
Abstract
Deidentifying sensitive information in electronic health records (EHRs) is increasingly important as legal obligations to data privacy evolve along with the need to protect patient and institutional confidentiality. This study aims to comparatively evaluate the performance of two state-of-the-art deidentification systems, Deduce and [...] Read more.
Deidentifying sensitive information in electronic health records (EHRs) is increasingly important as legal obligations to data privacy evolve along with the need to protect patient and institutional confidentiality. This study aims to comparatively evaluate the performance of two state-of-the-art deidentification systems, Deduce and Deidentify, on both real-world and synthetic Dutch medical texts, thereby providing insights into their relative strengths and limitations in preserving privacy while maintaining data utility. We employ a replication-extension research design, utilizing two distinct datasets: (1) the Annotation-Based Dataset from the Utrecht University Medical Center (UMC Utrecht), comprising manually annotated patient records spanning 1987 to 2021, and (2) the Synthetic Dataset, generated using a two-step process involving OpenAI’s GPT-4 model. Utilizing precision, recall, and F1 scores as evaluation metrics, we uncover the relative strengths and limitations of the two methods. Our findings indicate that both techniques show variable performance across different entities of deidentifying text information. Deduce outperforms Deidentify in overall accuracy by a margin of 0.42 on the synthetic datasets. On the real-world annotation-based dataset, the generalization ability of Deidentify is lower than Deduce by 0.2. However, the performance of both techniques is affected by the limitations of the dataset. In conclusion, this study provides valuable insights into the comparative performance of Deduce and Deidentify for deidentifying Dutch EHRs, contributing to the development of more effective privacy preservation techniques in the healthcare domain. Full article
(This article belongs to the Special Issue Digital Security and Privacy Protection: Trends and Applications, 2nd Edition)
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20 pages, 523 KiB  
Article
Navigating the CISO’s Mind by Integrating GenAI for Strategic Cyber Resilience
by Šarūnas Grigaliūnas, Rasa Brūzgienė, Kęstutis Driaunys, Renata Danielienė, Ilona Veitaitė, Paulius Astromskis, Živilė Nemickienė, Dovilė Vengalienė, Audrius Lopata, Ieva Andrijauskaitė and Neringa Gaubienė
Electronics 2025, 14(7), 1342; https://doi.org/10.3390/electronics14071342 - 27 Mar 2025
Viewed by 292
Abstract
AI-driven cyber threats are evolving faster than current defense mechanisms, complicating forensic investigations. As attacks grow more sophisticated, forensic methods struggle to analyze vast wearable device data, highlighting the need for an advanced framework to improve threat detection and responses. This paper presents [...] Read more.
AI-driven cyber threats are evolving faster than current defense mechanisms, complicating forensic investigations. As attacks grow more sophisticated, forensic methods struggle to analyze vast wearable device data, highlighting the need for an advanced framework to improve threat detection and responses. This paper presents a generative artificial intelligence (GenAI)-assisted framework that enhances cyberforensics and strengthens strategic cyber resilience, particularly for chief information security officers (CISOs). It addresses three key challenges: inefficient incident reconstruction, open-source intelligence (OSINT) limitations, and real-time decision-making difficulties. The framework integrates GenAI to automate routine tasks, the cross-layering of digital attributes from wearable devices and open-source intelligence (OSINT) to provide a comprehensive understanding of malicious incidents. By synthesizing digital attributes and applying the 5W approach, the framework facilitates accurate incident reconstruction, enabling CISOs to respond to threats with improved precision. The proposed framework is validated through experimental testing involving publicly available wearable device datasets (e.g., GPS data, pairing and activity logs). The results show that GenAI enhances incident detection and reconstruction, increasing the accuracy and speed of CISOs’ responses to threats. The experimental evaluation demonstrates that our framework improves cyberforensics efficiency by streamlining the integration of digital attributes, reducing the incident reconstruction time and enhancing decision-making precision. The framework enhances cybersecurity resilience in critical infrastructures, although challenges remain regarding data privacy, accuracy and scalability. Full article
(This article belongs to the Special Issue Digital Security and Privacy Protection: Trends and Applications, 2nd Edition)
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29 pages, 3751 KiB  
Article
Proximal Policy-Guided Hyperparameter Optimization for Mitigating Model Decay in Cryptocurrency Scam Detection
by Su-Hwan Choi, Sang-Min Choi and Seok-Jun Buu
Electronics 2025, 14(6), 1192; https://doi.org/10.3390/electronics14061192 - 18 Mar 2025
Viewed by 356
Abstract
As cryptocurrency transactions continue to grow, detecting scams within transaction records remains a critical challenge. These transactions can be represented as dynamic graphs, where Neural Network Convolution (NNConv) models are widely used for detection. However, NNConv models suffer from model decay due to [...] Read more.
As cryptocurrency transactions continue to grow, detecting scams within transaction records remains a critical challenge. These transactions can be represented as dynamic graphs, where Neural Network Convolution (NNConv) models are widely used for detection. However, NNConv models suffer from model decay due to evolving transaction patterns, the introduction of new users, and the emergence of adversarial techniques designed to evade detection. To address this issue, we propose an automated, periodic hyperparameter optimization method based on proximal policy optimization (PPO), a reinforcement learning algorithm designed for dynamic environments. By leveraging PPO’s stable policy updates and efficient exploration strategies, our approach continuously refines hyperparameters to sustain model performance without frequent retraining. We evaluate the proposed method on a large-scale cryptocurrency transaction dataset containing 2,973,489 nodes and 13,551,303 edges. The results demonstrate that our method achieves an F1 score of 0.9478, outperforming existing graph-based approaches. These findings validate the effectiveness of PPO-based optimization in mitigating model decay and ensuring robust cryptocurrency scam detection. Full article
(This article belongs to the Special Issue Digital Security and Privacy Protection: Trends and Applications, 2nd Edition)
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18 pages, 2503 KiB  
Article
Reinforced Disentangled HTML Representation Learning with Hard-Sample Mining for Phishing Webpage Detection
by Jun-Ho Yoon, Seok-Jun Buu and Hae-Jung Kim
Electronics 2025, 14(6), 1080; https://doi.org/10.3390/electronics14061080 - 9 Mar 2025
Viewed by 486
Abstract
Phishing webpage detection is critical in combating cyber threats, yet distinguishing between benign and phishing webpages remains challenging due to significant feature overlap in the representation space. This study introduces a reinforced Triplet Network to optimize disentangled representation learning tailored for phishing detection. [...] Read more.
Phishing webpage detection is critical in combating cyber threats, yet distinguishing between benign and phishing webpages remains challenging due to significant feature overlap in the representation space. This study introduces a reinforced Triplet Network to optimize disentangled representation learning tailored for phishing detection. By employing reinforcement learning, the method enhances the sampling of anchor, positive, and negative examples, addressing a core limitation of traditional Triplet Networks. The disentangled representations generated through this approach provide a clear separation between benign and phishing webpages, substantially improving detection accuracy. To achieve comprehensive modeling, the method integrates multimodal features from both URLs and HTML DOM Graph structures. The evaluation leverages a real-world dataset comprising over one million webpages, meticulously collected for diverse and representative phishing scenarios. Experimental results demonstrate a notable improvement, with the proposed method achieving a 6.7% gain in the F1 score over state-of-the-art approaches, highlighting its superior capability and the dataset’s critical role in robust performance. Full article
(This article belongs to the Special Issue Digital Security and Privacy Protection: Trends and Applications, 2nd Edition)
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22 pages, 839 KiB  
Article
A Randomized Response Framework to Achieve Differential Privacy in Medical Data
by Andreas Ioannidis, Antonios Litke and Nikolaos K. Papadakis
Electronics 2025, 14(2), 326; https://doi.org/10.3390/electronics14020326 - 15 Jan 2025
Cited by 1 | Viewed by 725
Abstract
In recent years, differential privacy has gained substantial traction in the medical domain, where the need to balance privacy preservation with data utility is paramount. As medical data increasingly relies on cloud platforms and distributed sharing among multiple stakeholders, such as healthcare providers, [...] Read more.
In recent years, differential privacy has gained substantial traction in the medical domain, where the need to balance privacy preservation with data utility is paramount. As medical data increasingly relies on cloud platforms and distributed sharing among multiple stakeholders, such as healthcare providers, researchers, and policymakers, the importance of privacy-preserving techniques has become more pronounced. Trends in the field focus on designing efficient algorithms tailored to high-dimensional medical datasets, incorporating privacy guarantees into federated learning for distributed medical devices, and addressing challenges posed by adversarial attacks. Our work lays a foundation for these emerging applications by emphasizing the role of randomized response within the broader differential privacy framework, paving the way for advancements in secure medical data sharing and analysis. In this paper, we analyze the classical concept of a randomized response and investigate how it relates to the fundamental concept of differential privacy. Our approach is both mathematical and algorithmic in nature, and our purpose is twofold. On the one hand, we provide a formal and precise definition of differential privacy within a natural and convenient probabilistic—statistical framework. On the other hand, we position a randomized response as a special yet significant instance of differential privacy, demonstrating its utility in preserving individual privacy in sensitive data scenarios. To substantiate our findings, we include key theoretical proofs and provide indicative simulations, accompanied by open-access code to facilitate reproducibility and further exploration. Full article
(This article belongs to the Special Issue Digital Security and Privacy Protection: Trends and Applications, 2nd Edition)
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21 pages, 1040 KiB  
Article
AIDS-Based Cyber Threat Detection Framework for Secure Cloud-Native Microservices
by Heeji Park, Abir EL Azzaoui and Jong Hyuk Park
Electronics 2025, 14(2), 229; https://doi.org/10.3390/electronics14020229 - 8 Jan 2025
Viewed by 1302
Abstract
Cloud-native architectures continue to redefine application development and deployment by offering enhanced scalability, performance, and resource efficiency. However, they present significant security challenges, particularly in securing inter-container communication and mitigating Distributed Denial of Service (DDoS) attacks in containerized microservices. This study proposes an [...] Read more.
Cloud-native architectures continue to redefine application development and deployment by offering enhanced scalability, performance, and resource efficiency. However, they present significant security challenges, particularly in securing inter-container communication and mitigating Distributed Denial of Service (DDoS) attacks in containerized microservices. This study proposes an Artificial Intelligence Intrusion Detection System (AIDS)-based cyber threat detection solution to address these critical security challenges inherent in cloud-native environments. By leveraging a Resilient Backpropagation Neural Network (RBN), the proposed solution enhances system security and resilience by effectively detecting and mitigating DDoS attacks in real time in both the network and application layers. The solution incorporates an Inter-Container Communication Bridge (ICCB) to ensure secure communication between containers. It also employs advanced technologies such as eXpress Data Path (XDP) and the Extended Berkeley Packet Filter (eBPF) for high-performance and low-latency security enforcement, thereby overcoming the limitations of existing research. This approach provides robust protection against evolving security threats while maintaining the dynamic scalability and efficiency of cloud-native architectures. Furthermore, the system enhances operational continuity through proactive monitoring and dynamic adaptability, ensuring effective protection against evolving threats while preserving the inherent scalability and efficiency of cloud-native environments. Full article
(This article belongs to the Special Issue Digital Security and Privacy Protection: Trends and Applications, 2nd Edition)
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22 pages, 11189 KiB  
Article
VUF-MIWS: A Visible and User-Friendly Watermarking Scheme for Medical Images
by Chia-Chen Lin, Yen-Heng Lin, En-Ting Chu, Wei-Liang Tai and Chun-Jung Lin
Electronics 2025, 14(1), 122; https://doi.org/10.3390/electronics14010122 - 30 Dec 2024
Viewed by 779
Abstract
The integration of Internet of Medical Things (IoMT) technology has revolutionized healthcare, allowing rapid access to medical images and enhancing remote diagnostics in telemedicine. However, this advancement raises serious cybersecurity concerns, particularly regarding unauthorized access and data integrity. This paper presents a novel, [...] Read more.
The integration of Internet of Medical Things (IoMT) technology has revolutionized healthcare, allowing rapid access to medical images and enhancing remote diagnostics in telemedicine. However, this advancement raises serious cybersecurity concerns, particularly regarding unauthorized access and data integrity. This paper presents a novel, user-friendly, visible watermarking scheme for medical images—Visual and User-Friendly Medical Image Watermarking Scheme (VUF-MIWS)—designed to secure medical image ownership while maintaining usability for diagnostic purposes. VUF-MIWS employs a unique combination of inpainting and data hiding techniques to embed hospital logos as visible watermarks, which can be removed seamlessly once image authenticity is verified, restoring the image to its original state. Experimental results demonstrate the scheme’s robust performance, with the watermarking process preserving critical diagnostic information with high fidelity. The method achieved Peak Signal-to-Noise Ratios (PSNR) above 70 dB and Structural Similarity Index Measures (SSIM) of 0.99 for inpainted images, indicating minimal loss of image quality. Additionally, VUF-MIWS effectively restored the ROI region of medical images post-watermark removal, as verified through test cases with restored watermarked regions matching the original images. These findings affirm VUF-MIWS’s suitability for secure telemedicine applications. Full article
(This article belongs to the Special Issue Digital Security and Privacy Protection: Trends and Applications, 2nd Edition)
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15 pages, 5973 KiB  
Article
Investigating Digital Forensic Artifacts Generated from 3D Printing Slicing Software: Windows and Linux Analysis
by Laura Garland, Ashar Neyaz, Cihan Varol and Narasimha K. Shashidhar
Electronics 2024, 13(14), 2864; https://doi.org/10.3390/electronics13142864 - 20 Jul 2024
Viewed by 1193
Abstract
Although Three-dimensional (3D) printers have legitimate applications in various fields, they also present opportunities for misuse by criminals who can infringe upon intellectual property rights, manufacture counterfeit medical products, or create unregulated and untraceable firearms. The rise of affordable 3D printers for general [...] Read more.
Although Three-dimensional (3D) printers have legitimate applications in various fields, they also present opportunities for misuse by criminals who can infringe upon intellectual property rights, manufacture counterfeit medical products, or create unregulated and untraceable firearms. The rise of affordable 3D printers for general consumers has exacerbated these concerns, making it increasingly vital for digital forensics investigators to identify and analyze vital artifacts associated with 3D printing. In our study, we focus on the identification and analysis of digital forensic artifacts related to 3D printing stored in both Linux and Windows operating systems. We create five distinct scenarios and gather data, including random-access memory (RAM), configuration data, generated files, residual data, and network data, to identify when 3D printing occurs on a device. Furthermore, we utilize the 3D printing slicing software Ultimaker Cura version 5.7 and RepetierHost version 2.3.2 to complete our experiments. Additionally, we anticipate that criminals commonly engage in anti-forensics and recover valuable evidence after uninstalling the software and deleting all other evidence. Our analysis reveals that each data type we collect provides vital evidence relating to 3D printing forensics. Full article
(This article belongs to the Special Issue Digital Security and Privacy Protection: Trends and Applications, 2nd Edition)
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Review

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28 pages, 1368 KiB  
Review
IoT–Cloud Integration Security: A Survey of Challenges, Solutions, and Directions
by Mohammed Almutairi and Frederick T. Sheldon
Electronics 2025, 14(7), 1394; https://doi.org/10.3390/electronics14071394 - 30 Mar 2025
Viewed by 696
Abstract
The confluence of the Internet of Things (IoT) and cloud computing heralds a paradigm shift in data-driven applications, promising unprecedented insights and automation across critical sectors like healthcare, smart cities, and industrial automation. However, this transformative synergy introduces a complex tapestry of security [...] Read more.
The confluence of the Internet of Things (IoT) and cloud computing heralds a paradigm shift in data-driven applications, promising unprecedented insights and automation across critical sectors like healthcare, smart cities, and industrial automation. However, this transformative synergy introduces a complex tapestry of security vulnerabilities stemming from the intrinsic resource limitations of IoT devices and the inherent complexities of cloud infrastructures. This survey delves into the escalating threats—from conventional data breaches and Application programming interface (API) exploits to emerging vectors such as adversarial artificial intelligence (AI), quantum-resistant attacks, and sophisticated insider threats—that imperil the integrity and resilience of IoT–cloud ecosystems. We critically evaluated existing security paradigms, including encryption, access control, and service-level agreements, juxtaposed with cutting-edge approaches like AI-driven anomaly detection, blockchain-secured frameworks, and lightweight cryptographic solutions. By systematically mapping the landscape of security challenges and mitigation strategies, this work identified the following critical research imperatives: the development of standardized, end-to-end security architectures, the integration of post-quantum cryptography for resource-constrained IoT devices, and the fortification of resource isolation in multi-tenant cloud environments. A comprehensive comparative analysis of prior research, coupled with an in-depth case study on IoT–cloud security within the healthcare domain, illuminates the practical challenges and innovative solutions crucial for real-world deployment. Ultimately, this survey advocates for the development of scalable, adaptive security frameworks that leverage the synergistic power of AI and blockchain, ensuring the secure and efficient evolution of IoT–cloud ecosystems in the face of evolving cyber threats. Full article
(This article belongs to the Special Issue Digital Security and Privacy Protection: Trends and Applications, 2nd Edition)
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