Advanced Technologies for Information Security and Privacy

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (25 October 2023) | Viewed by 2119

Special Issue Editors


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Guest Editor
Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, 7522 NB Enschede, The Netherlands
Interests: information security; service oriented computing

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Guest Editor
Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, 7522 NB Enschede, The Netherlands
Interests: compliance checking; process mining; information auditing
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, 7522 NB Enschede, The Netherlands
Interests: computer science; information security

Special Issue Information

Dear Colleagues,

Information is the most valuable asset in the digital era. Privacy is the right to view, use, and manage data, while security is protecting information against threats and danger. Therefore, information security and privacy are top concerns in any domain and in any industry for both industrial and personal data, and numerous advanced technologies are emerging, aiming to enhance the security of information and ensure privacy.

The Special Issue of Applied Sciences (ISSN 2076-3417) on ‘Advanced Technologies for Information Security and Privacy’ aims to collect research contributions from a wide range of disciplines and domains directly or indirectly related to advanced technologies used in information security and privacy. We invite contributions ranging from theoretical or conceptual papers to technical algorithmic ones, as well as applications and case studies. Topics include but are not limited to:

  • Advanced technologies used to enhance information security;
  • Advanced technologies used to ensure data privacy;
  • AI-driven information security;
  • AI-influenced data privacy;
  • Information security in embedded systems;
  • Blockchain technologies for security of information;
  • IoT-based information security application;
  • Machine learning in information security;
  • Connectors’ role in the data space, with a security perspective;
  • Privacy-enhancing technologies;
  • Machine learning in data privacy;
  • Future technologies in data privacy.

Dr. Jeewanie Jayasinghe Arachchige
Dr. Faiza Allah Bukhsh
Dr. Maya Daneva
Guest Editors

Manuscript Submission Information

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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. Applied Sciences 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

  • information security
  • privacy
  • AI-driven information security
  • AI-driven data privacy
  • connectors
  • blockchain-enabled information security

Published Papers (2 papers)

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Research

16 pages, 756 KiB  
Article
Secure Proxy Re-Encryption Protocol for FANETs Resistant to Chosen-Ciphertext Attacks
by Hyun-A Park
Appl. Sci. 2024, 14(2), 761; https://doi.org/10.3390/app14020761 - 16 Jan 2024
Viewed by 460
Abstract
In emergency situations, ensuring the secure transmission of medical information is critical. While existing schemes address on-road emergencies, off-road scenarios present unique challenges due to hazardous locations inaccessible to conventional vehicles. This research introduces a protocol for off-road emergencies, leveraging flying ad hoc [...] Read more.
In emergency situations, ensuring the secure transmission of medical information is critical. While existing schemes address on-road emergencies, off-road scenarios present unique challenges due to hazardous locations inaccessible to conventional vehicles. This research introduces a protocol for off-road emergencies, leveraging flying ad hoc networks (FANETs) formed by drones. The protocol, designed for users receiving emergency treatment, employs cryptographic techniques to protect sensitive information. To overcome the challenge of decrypting user medical records at emergency centers without the healthcare provider’s key, proxy re-encryption is employed. The control center (CC) securely generates encryption and decryption keys, facilitating the re-encryption process by the cloud server (CS) and transmission to the emergency center (E). The proposed protocol, free from pairing functions, underwent security and efficiency analyses, demonstrating resilience against chosen-ciphertext attacks (CCA) and collusion resistance (CR). Execution times of approximately 0.02 and 0.0 s for re-encryption and decryption processes, respectively, for a message size of 2000 bytes highlighted the efficiency of the protocol. The research contributes a secure and efficient proxy re-encryption protocol for off-road emergency medical information transmission within FANETs. Full article
(This article belongs to the Special Issue Advanced Technologies for Information Security and Privacy)
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23 pages, 762 KiB  
Article
Enhanced Adaptable and Distributed Access Control Decision Making Model Based on Machine Learning for Policy Conflict Resolution in BYOD Environment
by Aljuaid Turkea Ayedh M, Ainuddin Wahid Abdul Wahab and Mohd Yamani Idna Idris
Appl. Sci. 2023, 13(12), 7102; https://doi.org/10.3390/app13127102 - 14 Jun 2023
Viewed by 1195
Abstract
Organisations are adopting new IT strategies such as “Bring Your Own Device” (BYOD) and remote working. These trends are highly beneficial both for enterprise owners and employees in terms of increased productivity and reduced costs. However, security issues such as unauthorised access as [...] Read more.
Organisations are adopting new IT strategies such as “Bring Your Own Device” (BYOD) and remote working. These trends are highly beneficial both for enterprise owners and employees in terms of increased productivity and reduced costs. However, security issues such as unauthorised access as well as privacy concerns pose significant obstacles. These can be overcome by adopting access control techniques and a dynamic security and privacy policy that governs these issues where they arise. Policy decision points in traditional access control systems, such as role-based access control (RBAC), attribute-based access control (ABAC), or relationship-based access control (ReBAC), may be limited because the status of access control can vary in response to minor changes in user and resource properties. As a result, system administrators rely on a solution for constructing complex rules with many conditions and permissions for decision control. This results in access control issues, including policy conflicts, decision-making bottlenecks, delayed access response times and mediocre performance. This paper proposes a policy decision-making and access control-based supervised learning algorithm. The algorithm enhances policy decision points (PDPs). This is achieved by transforming the PDP’s problem into a binary classification for security access control that either grants or denies access requests. Also, a vector decision classifier based on the supervised machine learning algorithm is developed to generate an accurate, effective, distributed and dynamic policy decision point (PDP). Performance was evaluated using the Kaggle-Amazon access control policy dataset, which compared the effectiveness of the proposed mechanism to previous research benchmarks in terms of performance, time and flexibility. The proposed solution obtains a high level of privacy for access control policies because the PDP does not communicate directly with the policy administration point (PAP). In conclusion, PDP-based ML generates accurate decisions and can simultaneously fulfill multiple massive policies and huge access requests with 95% Accuracy in a short response time of around 0.15 s without policy conflicts. Access control security is improved by making it dynamic, adaptable, flexible and distributed. Full article
(This article belongs to the Special Issue Advanced Technologies for Information Security and Privacy)
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