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Data Privacy, Security, and Trust in New Technological Trends—2nd Edition
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Data Privacy, Security, and Trust in New Technological Trends—2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 23624

Special Issue Editor

Prof. Dr. Valderi R. Q. Leithardt

E-Mail Website
Guest Editor
Department of Information Science and Technology, ISTAR, University Institute of Lisbon (ISCTE-IUL), 1649-026 Lisbon, Portugal
Interests: distributed systems; algorithms; data privacy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of sensors into a wide variety of connected devices will enable the expansion of IoT, enabling more comprehensive data collection to improve automation and decision making. As a result, sensors on mobile devices will become smarter, using machine learning and artificial intelligence algorithms to improve efficiency in data collection and interpretation. Therefore, the use of Heterogeneous Networks like 6G will likely adopt a more integrated approach, combining ground, air, and space communications to create a more heterogeneous and comprehensive network. Thus, there is a need for more robust solutions related to the security of applications and systems in different contexts of use and processing. Therefore, it is necessary to implement and improve solutions that involve security and data encryption in a context not only of software but also hardware, whether in a mini-actualization scenario or in large servers for large-scale data processing, thus making data privacy management more extensive, whether for the user or for commercial or industrial solutions. With this, we invite authors with proposals for solutions that solve these open problems with news and scientific contributions for the readers of this Special Issue and for the scientific community.

Prof. Dr. Valderi R. Q. Leithardt
Guest Editor

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Keywords

  • blockchain
  • communication protocols
  • cryptographic algorithms
  • cyberattacks
  • cybersecurity
  • data privacy and trust
  • data security on drones
  • knowledge on security and privacy
  • hardware security
  • data security in smart cities
  • information context
  • Internet of Things
  • management data privacy and trust in cloud computing
  • mechanism to protect mobile and IoT applications
  • security in data science

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Published Papers (6 papers)

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Research

30 pages, 490 KB  
Article
Adaptive Threat Mitigation in PoW Blockchains (Part II): A Deep Reinforcement Learning Approach to Countering Evasive Adversaries
by Rafał Skowroński
Sensors 2026, 26(4), 1368; https://doi.org/10.3390/s26041368 - 21 Feb 2026
Viewed by 499
Abstract
Static defense mechanisms in blockchain security, while effective against known threats, are inherently vulnerable to intelligent adversaries who can adapt their strategies to evade detection. This paper addresses this critical limitation by proposing a next-generation adaptive security framework powered by deep reinforcement learning [...] Read more.
Static defense mechanisms in blockchain security, while effective against known threats, are inherently vulnerable to intelligent adversaries who can adapt their strategies to evade detection. This paper addresses this critical limitation by proposing a next-generation adaptive security framework powered by deep reinforcement learning (DRL). Building upon the state-of-the-art statistical detection system presented in Part I of this series, we introduce a DRL agent that learns to dynamically adjust security parameters in response to evolving network conditions and adversarial behavior. The agent is trained using a realistic, proxy-based reward function that optimizes for network stability without requiring ground-truth attack labels. We conduct comprehensive evaluation across multiple scenarios, demonstrating that our DRL-enhanced framework consistently renders attacks unprofitable where static models eventually fail. Against adaptive adversaries, the DRL agent drives adversary profit to −42±13% (deeply unprofitable) compared to +65±22% (profitable) under the static framework and +145±18% under baseline detectors. Furthermore, we demonstrate resilience in zero-day scenarios where novel attack variants are suppressed within 24 h, and compare performance against alternative AI methodologies (supervised learning, GANs), achieving a superior F1-score of 0.95±0.02. This work provides a robust blueprint for creating intelligent, adaptive, and resilient security systems for future decentralized networks. Full article
(This article belongs to the Special Issue Data Privacy, Security, and Trust in New Technological Trends—2nd Edition)
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26 pages, 566 KB  
Article
Relational Framework of Cyberattacks: Empirical Evidence from Multistage Incidents
by Mikel Ferrer-Oliva, José-Amelio Medina-Merodio, José-Javier Martínez-Herraiz and Carlos Cilleruelo-Rodríguez
Sensors 2025, 25(23), 7124; https://doi.org/10.3390/s25237124 - 21 Nov 2025
Viewed by 1531
Abstract
The increasing scale and operational complexity of cyberattacks have exposed the limitations of static taxonomies for representing multistage threat scenarios. This study addresses the need for more flexible classification models by proposing a relational taxonomy of cyberattacks grounded in documented incidents. Therefore, the [...] Read more.
The increasing scale and operational complexity of cyberattacks have exposed the limitations of static taxonomies for representing multistage threat scenarios. This study addresses the need for more flexible classification models by proposing a relational taxonomy of cyberattacks grounded in documented incidents. Therefore, the main objective is to propose a relational taxonomy that encodes direct transitions across eight groups in a dependency matrix and a directed graph while preserving traceability to MITRE ATT&CK. The taxonomy was validated by an independent panel of experts who assessed methodological clarity and operational utility. The results reveal consistent transition patterns across groups, delineate reproducible escalation routes, and pinpoint cut-off points linked to specific detection and control activities, providing an operational map of progression and intervention. The conclusions show that the taxonomy clarifies escalation paths, strengthens alignment across security monitoring and incident response functions, threat intelligence workflows and training, and provides an operational structure to manage interdependencies, anticipate escalation and focus monitoring on critical points. Full article
(This article belongs to the Special Issue Data Privacy, Security, and Trust in New Technological Trends—2nd Edition)
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22 pages, 1978 KB  
Article
Evading Antivirus Detection Using Fountain Code-Based Techniques for Executing Shellcodes
by Gang-Cheng Huang, Ko-Chin Chang and Tai-Hung Lai
Sensors 2025, 25(2), 460; https://doi.org/10.3390/s25020460 - 15 Jan 2025
Cited by 3 | Viewed by 5832
Abstract
In this study, we propose a method for successfully evading antivirus detection by encoding malicious shellcode with fountain codes. The Meterpreter framework for Microsoft Windows 32-bit and 64-bit architectures was used to produce the shellcode used in this investigation. The experimental results proved [...] Read more.
In this study, we propose a method for successfully evading antivirus detection by encoding malicious shellcode with fountain codes. The Meterpreter framework for Microsoft Windows 32-bit and 64-bit architectures was used to produce the shellcode used in this investigation. The experimental results proved that detection rates were substantially decreased. Specifically, the number of detected instances using antivirus vendors for 32-bit shellcode decreased from 18 to 3, while for 64-bit shellcode, it decreased from 16 to 1. This method breaks up a malicious payload into many packets, each with their own distinct structure, and then encodes them. This obfuscation approach maintains the shellcode’s integrity, ensuring correct code execution. However, in the persistence phase of the penetration testing process, this method offers an additional means of evading antivirus techniques. Full article
(This article belongs to the Special Issue Data Privacy, Security, and Trust in New Technological Trends—2nd Edition)
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38 pages, 1147 KB  
Article
Seamless Transition to Post-Quantum TLS 1.3: A Hybrid Approach Using Identity-Based Encryption
by Thiago Leucz Astrizi  and Ricardo Custódio 
Sensors 2024, 24(22), 7300; https://doi.org/10.3390/s24227300 - 15 Nov 2024
Cited by 5 | Viewed by 7747
Abstract
We propose a novel solution to streamline the migration of existing Transport Layer Security (TLS) protocol implementations to a post-quantum Key Encapsulation Mechanism for Transport Layer Security (KEMTLS). By leveraging Identity-Based Encryption (IBE), our solution minimizes the necessary modifications to the surrounding infrastructure, [...] Read more.
We propose a novel solution to streamline the migration of existing Transport Layer Security (TLS) protocol implementations to a post-quantum Key Encapsulation Mechanism for Transport Layer Security (KEMTLS). By leveraging Identity-Based Encryption (IBE), our solution minimizes the necessary modifications to the surrounding infrastructure, enabling the reuse of existing keys and certificates. We provide a proof-of-concept implementation and performance analysis, demonstrating the practical feasibility and effectiveness of our proposed approach. Full article
(This article belongs to the Special Issue Data Privacy, Security, and Trust in New Technological Trends—2nd Edition)
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14 pages, 3040 KB  
Article
User Privacy Protection via Windows Registry Hooking and Runtime Encryption
by Edward L. Amoruso, Richard Leinecker and Cliff C. Zou
Sensors 2024, 24(16), 5106; https://doi.org/10.3390/s24165106 - 6 Aug 2024
Viewed by 2887
Abstract
The Windows registry contains a plethora of information in a hierarchical database. It includes system-wide settings, user preferences, installed programs, and recently accessed files and maintains timestamps that can be used to construct a detailed timeline of user activities. However, these data are [...] Read more.
The Windows registry contains a plethora of information in a hierarchical database. It includes system-wide settings, user preferences, installed programs, and recently accessed files and maintains timestamps that can be used to construct a detailed timeline of user activities. However, these data are unencrypted and thus vulnerable to exploitation by malicious actors who gain access to this repository. To address this security and privacy concern, we propose a novel approach that efficiently encrypts and decrypts sensitive registry data in real time. Our developed proof-of-concept program intercepts interactions between the registry’s application programming interfaces (APIs) and other Windows applications using an advanced hooking technique. This enables the proposed system to be transparent to users without requiring any changes to the operating system or installed software. Our approach also implements the data protection API (DPAPI) developed by Microsoft to securely manage each user’s encryption key. Ultimately, our research provides an enhanced security and privacy framework for the Windows registry, effectively fortifying the registry against security and privacy threats while maintaining its accessibility to legitimate users and applications. Full article
(This article belongs to the Special Issue Data Privacy, Security, and Trust in New Technological Trends—2nd Edition)
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25 pages, 1063 KB  
Article
Enhancing Monitoring Performance: A Microservices Approach to Monitoring with Spyware Techniques and Prediction Models
by Anubis Graciela de Moraes Rossetto, Darlan Noetzold, Luis Augusto Silva and Valderi Reis Quietinho Leithardt
Sensors 2024, 24(13), 4212; https://doi.org/10.3390/s24134212 - 28 Jun 2024
Cited by 2 | Viewed by 3749
Abstract
In today’s digital landscape, organizations face significant challenges, including sensitive data leaks and the proliferation of hate speech, both of which can lead to severe consequences such as financial losses, reputational damage, and psychological impacts on employees. This work considers a comprehensive solution [...] Read more.
In today’s digital landscape, organizations face significant challenges, including sensitive data leaks and the proliferation of hate speech, both of which can lead to severe consequences such as financial losses, reputational damage, and psychological impacts on employees. This work considers a comprehensive solution using a microservices architecture to monitor computer usage within organizations effectively. The approach incorporates spyware techniques to capture data from employee computers and a web application for alert management. The system detects data leaks, suspicious behaviors, and hate speech through efficient data capture and predictive modeling. Therefore, this paper presents a comparative performance analysis between Spring Boot and Quarkus, focusing on objective metrics and quantitative statistics. By utilizing recognized tools and benchmarks in the computer science community, the study provides an in-depth understanding of the performance differences between these two platforms. The implementation of Quarkus over Spring Boot demonstrated substantial improvements: memory usage was reduced by up to 80% and CPU usage by 95%, and system uptime decreased by 119%. This solution offers a robust framework for enhancing organizational security and mitigating potential threats through proactive monitoring and predictive analysis while also guiding developers and software architects in making informed technological choices. Full article
(This article belongs to the Special Issue Data Privacy, Security, and Trust in New Technological Trends—2nd Edition)
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