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Electronics
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Navigating the Digital Age: Security, Ethics and Trust in Emerging...
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Navigating the Digital Age: Security, Ethics and Trust in Emerging Technologies

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 15 February 2026 | Viewed by 11767

Special Issue Editors

Dr. Liyuan Liu

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Guest Editor
Department of Decision and System Sciences, Saint Joseph's University, Philadelphia, PA 19131, USA
Interests: artificial intelligence; machine learning; blockchain
Dr. Tong Qiao

E-Mail Website
Guest Editor
School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310005, China
Interests: AI security; multimedia forensics; data hiding
Dr. Zhuojun Duan

E-Mail Website
Guest Editor
College of Integrated Science and Engineering, James Madison University, Harrisonburg, VA 22807, USA
Interests: data analytics; algorithms; the internet of things
Special Issues, Collections and Topics in MDPI journals
Dr. Meng Han

E-Mail Website
Guest Editor
College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China
Interests: fintech; data mining; machine learning; big data; security and privacy
Special Issues, Collections and Topics in MDPI journals
Dr. Seyedamin Pouriyeh

E-Mail Website
Guest Editor
College of Computing and Software Engineering, Kennesaw State University, Marietta, GA 30060, USA
Interests: federated machine learning; cyber security; blockchain
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In today's digital landscape, security, ethics, and trust are crucial for the successful integration and sustainability of new technologies, such as advanced security algorithms, blockchain, artificial intelligence (AI), generative AI, quantum computing, and the Internet of things (IoT). These elements are essential in ensuring that technological advancements benefit society without compromising individual privacy, equity, or social values. Security remains essential in protecting sensitive information and strengthening our systems against an array of cyber threats. Ethical strategies in digital applications are critical, such as in designing algorithms to detect bias in AI and transparent systems to maintain integrity across applications. Trust represents a user's trust in the digital age, relying on transparent communication, consistent performance, and stringent accountability in terms of technological advancements.

This Special Issue invites discussions and research that not only push technological boundaries but also address the challenges related to ethics, security, and trust. This Specia Issue aims to spark a multidisciplinary conversation, contributing to the development of a digital environment that is innovative, secure, ethical, and trusted by all.

Topics of interest include, but are not limited to, the following:

  • Advanced security algorithms and their applications in protecting digital infrastructure.
  • Blockchain technology: implications for transparency, security, and trust.
  • Implementing security, ethics, and trust frameworks or systems in digital applications.
  • Challenges and opportunities for cybersecurity and data protection.
  • Trust-building mechanisms in Internet of things (IoT) ecosystems.
  • Privacy-preserving technologies and their role in enhancing user trust.
  • Surveying the emerging technologies in enhancing security, trust, and ethics in digital applications.
  • Robustness and reliability of AI systems in critical applications.
  • Applying technological innovations to foster equity, diversity, and inclusion.
  • Practical applications of technologies in advancing sustainability.
  • User-centric approaches to technology design and implementation.
  • Decentralized systems and their implications for security and privacy.
  • Innovations in digital identity and access management.

Dr. Liyuan Liu
Dr. Tong Qiao
Dr. Zhuojun Duan
Dr. Meng Han
Dr. Seyedamin Pouriyeh
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

  • trustworthiness
  • security
  • privacy
  • data ethics
  • emerging technologies

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (9 papers)

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Research

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16 pages, 816 KiB  
Article
Evaluating Moving Target Defense Methods Using Time to Compromise and Security Risk Metrics in IoT Networks
by Dilli Prasad Sharma
Electronics 2025, 14(11), 2205; https://doi.org/10.3390/electronics14112205 - 29 May 2025
Viewed by 1037
Abstract
The Internet of Things (IoT) networks face an increasing number of cyber threats due to their heterogeneous, distributed, and resource-constrained nature. Conventional static defense mechanisms are often inadequate against sophisticated and advanced persistent threats. Moving Target Defense (MTD) is a dynamic proactive security [...] Read more.
The Internet of Things (IoT) networks face an increasing number of cyber threats due to their heterogeneous, distributed, and resource-constrained nature. Conventional static defense mechanisms are often inadequate against sophisticated and advanced persistent threats. Moving Target Defense (MTD) is a dynamic proactive security method that increases system resilience by continuously changing the attack surface, thereby increasing uncertainty and complexity for attackers. In this paper, we evaluate the effectiveness of shuffling or diversity-based MTD methods using time-to-compromise and security risk metrics. We develop attack path-based mean time-to-compromise and security risk reduction metrics for assessing the effectiveness of MTD. These metrics provide a quantitative basis for evaluating how well MTD techniques delay successful compromises and lower overall security risk exposure. The performance of the deployed MTD mechanism is evaluated and discussed for different attacker skill levels and shuffling frequencies. Full article
(This article belongs to the Special Issue Navigating the Digital Age: Security, Ethics and Trust in Emerging Technologies)
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30 pages, 4052 KiB  
Article
The DtMin Protocol: Implementing Data Minimization Principles in Medical Information Sharing
by Hyun-A Park
Electronics 2025, 14(8), 1501; https://doi.org/10.3390/electronics14081501 - 8 Apr 2025
Viewed by 259
Abstract
This study proposes DtMin, a novel protocol for implementing data minimization principles in medical information sharing between healthcare providers (HCPs) and electronic health record providers (EHRPs). DtMin utilizes a multi-type encryption approach, combining attribute-based encryption (ABE) and hybrid encryption techniques. The protocol classifies [...] Read more.
This study proposes DtMin, a novel protocol for implementing data minimization principles in medical information sharing between healthcare providers (HCPs) and electronic health record providers (EHRPs). DtMin utilizes a multi-type encryption approach, combining attribute-based encryption (ABE) and hybrid encryption techniques. The protocol classifies patient data attributes into six categories based on sensitivity, consent status, and sharing requests. It then applies differential encryption methods to ensure only the intersection of patient-consented and EHRP-requested attributes is shared in decipherable form. DtMin’s security is formally analyzed and proven under the ICR-DB and ICR-IS security games. Performance analysis demonstrates efficiency across various data volumes and patient numbers. This study explores the integration of DtMin with advanced cryptographic techniques such as lattice-based ABE and lightweight ABE variants, which can potentially enhance its performance and security in complex healthcare environments. Furthermore, it proposes strategies for integrating DtMin with existing healthcare information systems and adapting it to future big data environments processing over 100,000 records. These enhancements and integration strategies position DtMin as a scalable and practical solution for implementing data minimization in diverse healthcare settings, from small clinics to large-scale health information exchanges. Full article
(This article belongs to the Special Issue Navigating the Digital Age: Security, Ethics and Trust in Emerging Technologies)
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26 pages, 3184 KiB  
Article
Enhancing the Resilience of a Federated Learning Global Model Using Client Model Benchmark Validation
by Algimantas Venčkauskas, Jevgenijus Toldinas, Nerijus Morkevičius, Ernestas Serkovas and Modestas Krištaponis
Electronics 2025, 14(6), 1215; https://doi.org/10.3390/electronics14061215 - 19 Mar 2025
Viewed by 462
Abstract
Federated learning (FL) makes it possible for users to share trained models with one another, thereby removing the necessity of publicly centralizing training data. One of the best and most cost-effective ways to connect users is through email. To steal sensitive information, spam [...] Read more.
Federated learning (FL) makes it possible for users to share trained models with one another, thereby removing the necessity of publicly centralizing training data. One of the best and most cost-effective ways to connect users is through email. To steal sensitive information, spam emails might trick users into visiting malicious websites or performing other fraudulent actions. The developed semantic parser creates email metadata datasets from multiple email corpuses and populates the email domain ontology to facilitate the privacy of the information contained in email messages. There is a new idea to make FL global models more resistant to Byzantine attacks. It involves accepting updates only from strong participants whose local model shows higher validation scores using benchmark datasets. The proposed approach integrates FL, the email domain-specific ontology, the semantic parser, and a collection of benchmark datasets from heterogeneous email corpuses. By giving meaning to the metadata of an email message, the email’s domain-specific ontology made it possible to create datasets for email benchmark corpuses and participant updates in a unified format with the same features. In order to avoid fraudulently modified client updates from being applied to the global model, the experimental results approved the proposed approach to strengthen the resiliency of an FL global model by utilizing client model benchmark validation. Full article
(This article belongs to the Special Issue Navigating the Digital Age: Security, Ethics and Trust in Emerging Technologies)
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18 pages, 997 KiB  
Article
A Target Permutation Test for Statistical Significance of Feature Importance in Differentiable Models
by Sanad Biswas, Nina Grundlingh, Jonathan Boardman, Joseph White and Linh Le
Electronics 2025, 14(3), 571; https://doi.org/10.3390/electronics14030571 - 31 Jan 2025
Viewed by 1681
Abstract
Statistical methods are crucial for a wide range of analytical processes, from exploration and explanation to prediction and inference. Over the years, there has been a major shift towards machine learning and artificial intelligence techniques due to their powerful capability in learning the [...] Read more.
Statistical methods are crucial for a wide range of analytical processes, from exploration and explanation to prediction and inference. Over the years, there has been a major shift towards machine learning and artificial intelligence techniques due to their powerful capability in learning the complex relationships between data. However, there is a disadvantage with these technologies in that mechanisms to explain the associations between a model’s input features and its output decision-making are far fewer than in statistics. This lack of transparency is among the major reasons that prevent machine learning from being more widely utilized in numerous application domains. Beyond inexplicability, the lack of mechanisms for effectively statistically assessing feature significance, such as parsimony or the complexity–performance tradeoff, further limits users’ control over machine learning models. With such motivation, we are proposing a target permutation process for determination of statistical feature importance in differentiable models and neural networks. Compared to methods in the current literature, the switch to target permutation allows for the assessment of all input features simultaneously and the test results are strengthened with a statistical p-value for each feature. In addition, our test does not require the assumption of independence among inputs, as is prevalent in other works. Lastly, we empirically show that our target permutation process can identify highly nonlinear associations between features and target while being resilient to multicollinearity. The features marked as insignificant can be removed with minimal impact, and can even result in improved predictive performance. Full article
(This article belongs to the Special Issue Navigating the Digital Age: Security, Ethics and Trust in Emerging Technologies)
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20 pages, 3556 KiB  
Article
Enhancing Real-Time Video Streaming Quality via MPT-GRE Multipath Network
by Naseer Al-Imareen and Gábor Lencse
Electronics 2025, 14(3), 497; https://doi.org/10.3390/electronics14030497 - 25 Jan 2025
Viewed by 1037
Abstract
The demand for real-time 4K video streaming has introduced technical challenges due to the high bandwidth, low latency, and minimal jitter required for high-quality user experience. Traditional single-path networks often fail to meet these requirements, especially under network congestion and packet loss conditions, [...] Read more.
The demand for real-time 4K video streaming has introduced technical challenges due to the high bandwidth, low latency, and minimal jitter required for high-quality user experience. Traditional single-path networks often fail to meet these requirements, especially under network congestion and packet loss conditions, which degrade video quality and disrupt streaming stability. This study evaluates Multipath tunnel- Generic Routing Encapsulation (MPT-GRE), a technology designed to address these challenges by enabling simultaneous data transmission across multiple network paths. By aggregating bandwidth and adapting dynamically to network conditions, MPT-GRE enhances resilience, maintains quality during network disruptions, and offers throughput nearly equal to the sum of its physical paths’ throughput. This feature ensures that even if one path fails, the technology seamlessly continues streaming through the remaining path, significantly reducing interruptions. We measured key video quality metrics to assess MPT-GRE’s performance: Structural Similarity Index Measure (SSIM), Mean Squared Error (MSE), and Peak Signal-to-Noise Ratio (PSNR). Our results confirm that the MPT-GRE tunnel effectively improves SSIM, PSNR, and reduces MSE compared to single-path streaming, offering a more stable, high-quality viewing experience. Our results indicate that analyzing the SSIM, MSE, and PSNR values for 4K video streaming using the MPT tunnel demonstrates a significant performance improvement compared to a single path. The improvement percentages of the SSIM and PSNR values for the MPT tunnel are (29.05% and 29.04%) higher than that of the single path, while MSE is reduced by 81.17% compared to the single path. Full article
(This article belongs to the Special Issue Navigating the Digital Age: Security, Ethics and Trust in Emerging Technologies)
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32 pages, 2525 KiB  
Article
Cyberthreats and Security Measures in Drone-Assisted Agriculture
by Kyriaki A. Tychola and Konstantinos Rantos
Electronics 2025, 14(1), 149; https://doi.org/10.3390/electronics14010149 - 2 Jan 2025
Cited by 2 | Viewed by 2249
Abstract
Nowadays, the use of Unmanned Aerial Vehicles (UAVs), or drones in agriculture for crop assessment and monitoring is a timely and important issue that concerns both researchers and farmers. Mapping agricultural land is imperative for making appropriate management decisions. As a result, the [...] Read more.
Nowadays, the use of Unmanned Aerial Vehicles (UAVs), or drones in agriculture for crop assessment and monitoring is a timely and important issue that concerns both researchers and farmers. Mapping agricultural land is imperative for making appropriate management decisions. As a result, the necessity of this technology is increasing, given its numerous benefits. However, as with any modern and automated technology, security concerns arise from various aspects. In this paper, we discuss cyberthreats to drones, as this technology is vulnerable to attackers during data collection, storage, and usage. Although various techniques and methods have been developed to address attacks on drones, this field remains in its infancy in many respects. This paper provides a comprehensive review of the security challenges associated with the use of agricultural drones. The security issues were thoroughly analyzed, with a particular focus on cybersecurity, categorized into four distinct levels: emerging threats, sensor vulnerabilities, hardware and software attacks, and communication-related threats. Additionally, we examined the limitations and challenges posed by cyberthreats to drone systems. Full article
(This article belongs to the Special Issue Navigating the Digital Age: Security, Ethics and Trust in Emerging Technologies)
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18 pages, 678 KiB  
Article
Privacy-Preserving Federated Learning-Based Intrusion Detection System for IoHT Devices
by Fatemeh Mosaiyebzadeh, Seyedamin Pouriyeh, Meng Han, Liyuan Liu, Yixin Xie, Liang Zhao and Daniel Macêdo Batista
Electronics 2025, 14(1), 67; https://doi.org/10.3390/electronics14010067 - 27 Dec 2024
Viewed by 1816
Abstract
In recent years, Internet of Healthcare Things (IoHT) devices have attracted significant attention from computer scientists, healthcare professionals, and patients. These devices enable patients, especially in areas without access to hospitals, to easily record and transmit their health data to medical staff via [...] Read more.
In recent years, Internet of Healthcare Things (IoHT) devices have attracted significant attention from computer scientists, healthcare professionals, and patients. These devices enable patients, especially in areas without access to hospitals, to easily record and transmit their health data to medical staff via the Internet. However, the analysis of sensitive health information necessitates a secure environment to safeguard patient privacy. Given the sensitivity of healthcare data, ensuring security and privacy is crucial in this sector. Federated learning (FL) provides a solution by enabling collaborative model training without sharing sensitive health data with third parties. Despite FL addressing some privacy concerns, the privacy of IoHT data remains an area needing further development. In this paper, we propose a privacy-preserving federated learning framework to enhance the privacy of IoHT data. Our approach integrates federated learning with ϵ-differential privacy to design an effective and secure intrusion detection system (IDS) for identifying cyberattacks on the network traffic of IoHT devices. In our FL-based framework, SECIoHT-FL, we employ deep neural network (DNN) including convolutional neural network (CNN) models. We assess the performance of the SECIoHT-FL framework using metrics such as accuracy, precision, recall, F1-score, and privacy budget (ϵ). The results confirm the efficacy and efficiency of the framework. For instance, the proposed CNN model within SECIoHT-FL achieved an accuracy of 95.48% and a privacy budget (ϵ) of 0.34 when detecting attacks on one of the datasets used in the experiments. To facilitate the understanding of the models and the reproduction of the experiments, we provide the explainability of the results by using SHAP and share the source code of the framework publicly as free and open-source software. Full article
(This article belongs to the Special Issue Navigating the Digital Age: Security, Ethics and Trust in Emerging Technologies)
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22 pages, 405 KiB  
Article
A Secure and Efficient Authentication Scheme for Large-Scale IoT Devices Based on Zero-Knowledge Proof
by Ziyi Su, Shiwei Wang, Hongliu Cai, Jiaxuan Huang, Yourong Chen, Xudong Zhang and Muhammad Alam
Electronics 2024, 13(18), 3735; https://doi.org/10.3390/electronics13183735 - 20 Sep 2024
Cited by 1 | Viewed by 1359
Abstract
Current authentication schemes based on zero-knowledge proof (ZKP) still face issues such as high computation costs, low efficiency, and security assurance difficulty. Therefore, we propose a secure and efficient authentication scheme (SEAS) for large-scale IoT devices based on ZKP. In the initialization phase, [...] Read more.
Current authentication schemes based on zero-knowledge proof (ZKP) still face issues such as high computation costs, low efficiency, and security assurance difficulty. Therefore, we propose a secure and efficient authentication scheme (SEAS) for large-scale IoT devices based on ZKP. In the initialization phase, the trusted authority creates prerequisites for device traceability and system security. Then, we propose a new registration method to ensure device anonymity. In the identity tracing and revocation phase, we revoke the real identity of abnormal devices by decrypting and updating group public keys, avoiding their access and reducing revocation costs. In the authentication phase, we check the arithmetic relationship between blind certificates, proofs, and other random data. We propose a new anonymous batch authentication method to effectively reduce computation costs, enhance authentication efficiency, and guarantee device authentication security. Security analysis and experimental results show that an SEAS can ensure security and effectively reduce verification time and energy costs. Its security and performance exceed existing schemes. Full article
(This article belongs to the Special Issue Navigating the Digital Age: Security, Ethics and Trust in Emerging Technologies)
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Review

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38 pages, 1507 KiB  
Review
A Comprehensive Analysis of Privacy-Preserving Solutions Developed for IoT-Based Systems and Applications
by Abdul Majeed, Sakshi Patni and Seong Oun Hwang
Electronics 2025, 14(11), 2106; https://doi.org/10.3390/electronics14112106 - 22 May 2025
Viewed by 562
Abstract
In recent years, a large number of Internet of Things (IoT)-based products, solutions, and services have emerged from the industry to enter the marketplace, improving the quality of service. With the wide adoption of IoT-based systems/applications in real scenarios, the privacy preservation (PP) [...] Read more.
In recent years, a large number of Internet of Things (IoT)-based products, solutions, and services have emerged from the industry to enter the marketplace, improving the quality of service. With the wide adoption of IoT-based systems/applications in real scenarios, the privacy preservation (PP) topic has garnered significant attention from both academia and industry; as a result, many PP solutions have been developed, tailored to IoT-based systems/applications. This paper provides an in-depth analysis of state-of-the-art (SOTA) PP solutions recently developed for IoT-based systems and applications. We delve into SOTA PP methods that preserve IoT data privacy and categorize them into two scenarios: on-device and cloud computing. We categorize the existing PP solutions into privacy-by-design (PbD), such as federated learning (FL) and split learning (SL), and privacy engineering solutions (PESs), such as differential privacy (DP) and anonymization, and we map them to IoT-driven applications/systems. We further summarize the latest SOTA methods that employ multiple PP techniques like ϵ-DP + anonymization or ϵ-DP + blockchain + FL (rather than employing just one) to preserve IoT data privacy in both PES and PbD categories. Lastly, we highlight quantum-based methods devised to enhance the security and/or privacy of IoT data in real-world scenarios. We discuss the status of current research in PP techniques for IoT data within the scope established for this paper, along with opportunities for further research and development. To the best of our knowledge, this is the first work that provides comprehensive knowledge about PP topics centered on the IoT, and which can provide a solid foundation for future research. Full article
(This article belongs to the Special Issue Navigating the Digital Age: Security, Ethics and Trust in Emerging Technologies)
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