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Cybersecurity: Advances in Security and Privacy Enhancing Technology

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

Deadline for manuscript submissions: 31 August 2025 | Viewed by 1896

Special Issue Editor


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Guest Editor
Department of Computer and Security, Sejong University, Seoul 05006, Republic of Korea
Interests: Internet of Things (IoT); security; AI-based video security; interoperability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cybersecurity is the technology of protecting networks, systems, and data from cyberattacks. These cyberattacks aim to gain unauthorized access to networks, systems, and data and cause damage such as changing, destroying, or stealing sensitive data in the systems. Therefore, cybersecurity should provide various measures, methods, and solutions to protect users and systems from diverse threats and vulnerabilities. To minimize the risks of cybersecurity, we can consider many aspects. For instance, we can follow the security-by-design architecture, in which all products (e.g., software, hardware, and network) and services can be designed and implemented to ensure that the key security properties (i.e., confidentiality, availability, integrity, authentication, and accountability) and privacy issues are maintained properly in all phases of development and maintenance. This Special Issue (SI) aims to identify the security and privacy-enhancing technologies for cybersecurity. We invite submissions in theoretical and experimental studies, as well as comprehensive review and survey papers.

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

  • Advanced fine-grained authentication and dynamic access control for cybersecurity;
  • Pseudonymization and encryption for cybersecurity;
  • The automated, flexible, encrypted control of data access;
  • Privacy-enhancing technology (PET) for cybersecurity;
  • Privacy strategies for sensitive information;
  • Security and privacy architecture for cybersecurity;
  • Artificial intelligence for cybersecurity;
  • Interoperability in cybersecurity.

Prof. Dr. Young-Gab Kim
Guest Editor

Manuscript Submission Information

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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
  • interoperability
  • metaverse
  • privacy-enhancing technology (pet)
  • security by design
  • security architecture

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Published Papers (1 paper)

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Research

21 pages, 1227 KiB  
Article
ROLQ-TEE: Revocable and Privacy-Preserving Optimal Location Query Based on Trusted Execution Environment
by Bao Li, Fucai Zhou, Jian Xu, Qiang Wang, Jiacheng Li and Da Feng
Appl. Sci. 2025, 15(3), 1641; https://doi.org/10.3390/app15031641 - 6 Feb 2025
Viewed by 709
Abstract
With the advent of cloud computing, outsourced computing has emerged as an increasingly popular strategy to reduce the burden of local computation. Optimal location query (OLQ) is a computationally intensive task in the domain of big data outsourcing, which is designed to determine [...] Read more.
With the advent of cloud computing, outsourced computing has emerged as an increasingly popular strategy to reduce the burden of local computation. Optimal location query (OLQ) is a computationally intensive task in the domain of big data outsourcing, which is designed to determine the optimal placement of a new facility from a set of candidate locations. However, location data are sensitive and cannot be shared with other enterprises, so privacy-preserving optimal location query becomes particularly important. Although some privacy-preserving works have been proposed, they still suffer from other challenges, such as irrevocable query permissions and high communication overhead. To overcome these challenges, we propose a revocable and privacy-preserving optimal location query scheme based on TEE (Trusted Execution Environment). We employ a basic hash structure within the TEE to compute the intersection data of both parties. We use the concept of reverse nearest neighbor (RNN) to assess the impact of candidates, and then select the optimal facility location. In addition, to implement the revocation of query permissions, we introduce a key refresh strategy that adopts identity and timestamp. We evaluate the performance of the proposed scheme using real datasets, and the experimental results indicate strong practicality. Full article
(This article belongs to the Special Issue Cybersecurity: Advances in Security and Privacy Enhancing Technology)
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