Security and Privacy in AI and Large Model-Driven 6G Networks

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

Deadline for manuscript submissions: 15 September 2025 | Viewed by 813

Special Issue Editors


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Guest Editor
Department of Electronics and Information, School of Cyberspace Security, Xi’an Jiaotong University, Xi’an 710049, China
Interests: information security; physical layer security; system security
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Guest Editor
Department of Software Engineering, East China Normal University, Shanghai 200062, China
Interests: big data security and privacy; blockchain-based applications; crowdsourcing/mobile crowdsensing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: applied cryptography; information security; data security; blockchain
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Special Issue Information

Dear Colleagues,

We are entering the era of 5G Beyond or 6G, where the mobile network has evolved from primarily connecting people to the Internet to facilitating connections between people, things, and AI agents. Consequently, it has become apparent that the security architecture of 5G falls short in meeting the security and privacy requirements of 6G networks. This Special Issue aims to tackle the critical challenges and opportunities surrounding security and privacy in the rapidly evolving landscape of 6G networks, applications, and services, arising from the integration of large-scale models and AI. Moreover, novel foundational theories and technologies such as blockchain, quantum cryptography, and semantic secure communications will be covered in this Special Issue. With a focus on original research articles and reviews, this issue aims to explore various dimensions of security and privacy in the context of 6G, fostering a deeper understanding of emerging threats and innovative solutions.

Research areas include (but are not limited to) the following:

  • Zero-trust security towards 6G networks;
  • Security and privacy in AI-driven 6G;
  • Security and privacy in large model-driven 6G;
  • Security and privacy in content-driven 6G;
  • Physical layer security in access networks;
  • Cross-layer security design for AI agents;
  • Blockchain and cryptography for 6G;
  • Security issue for compatibility of 4G/5G;
  • Attack traceability and security supervision towards 6G;
  • Network fairness issues and solutions;
  • Anonymous communication/networks and their supervision in 6G;
  • Security and privacy in mobile crowdsensing of 6G;
  • Data security and its property issue;
  • New attack model in 6G;
  • Prototype, testbeds, and performance evaluation. 

We look forward to receiving your contributions.

Dr. Yiliang Liu
Dr. Haiqin Wu
Prof. Dr. Yuan Zhang
Guest Editors

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Keywords

  • 6G
  • security
  • privacy
  • AI

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Published Papers (2 papers)

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Research

17 pages, 459 KiB  
Article
Flexible Resource Optimization for D2D XL-MIMO Communication via Adversarial Multi-Armed Bandit
by Zhaomin Jian, Chao Ma, Yunchao Song, Mengshuang Liu and Huibin Liang
Electronics 2025, 14(8), 1498; https://doi.org/10.3390/electronics14081498 - 8 Apr 2025
Viewed by 154
Abstract
Extremely large-scale multi-input and multi-output (XL-MIMO) communication, compared to conventional massive multi-input multi-output communication, can support more users and higher data throughput, thereby significantly improving its spectral efficiency and spatial multiplexing capabilities. This paper investigates the optimization of resource allocation for device-to-device (D2D) [...] Read more.
Extremely large-scale multi-input and multi-output (XL-MIMO) communication, compared to conventional massive multi-input multi-output communication, can support more users and higher data throughput, thereby significantly improving its spectral efficiency and spatial multiplexing capabilities. This paper investigates the optimization of resource allocation for device-to-device (D2D) multicast communication in XL-MIMO cellular networks. The “many-to-many” sharing model permits one subcarrier to be shared among multiple D2D groups (DGs) and each DG to reuse multiple subcarriers. The objective is to maximize the total multicast data rate of DGs while meeting the data rate requirements of cellular users. This optimization problem is formulated as a 0–1 mixed-integer nonlinear programming problem, with the challenge lying in the fact that adjusting the subcarriers and the power of the user equipment alters the network’s carrier occupation and interference relationships, thereby increasing computational complexity. To address this challenge, a phased strategy is proposed. Initially, subcarrier allocation and coarse power allocation are conducted for cellular users. Subsequently, an adversarial multi-player multi-armed bandit framework is employed, treating DGs as players and subcarrier and power combinations as arms, to maximize the total multicast data rate. An improved Exp3 algorithm is utilized for selecting the optimal combination of arms. Finally, precise power allocation for cellular users is conducted based on the allocation results of the DGs. A comparative analysis of various simulations confirms the superiority of our algorithm over the established heuristic subcarrier assignment and proposed power allocation (HSAPP) and the channel allocation scheme using full information of device locations (CAFIL) approaches. Full article
(This article belongs to the Special Issue Security and Privacy in AI and Large Model-Driven 6G Networks)
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12 pages, 1250 KiB  
Article
A Light Source Authentication Algorithm Based on the Delay and Sum of the Light Source Emission Sequence
by Deyue Zou, Linna Yan and Siyu Gao
Electronics 2025, 14(7), 1438; https://doi.org/10.3390/electronics14071438 - 2 Apr 2025
Viewed by 179
Abstract
Indoor visible light positioning is susceptible to specular interference, which reduces the accuracy of positioning. From the perspective of increasing the number of identity documents (IDs), this paper proposes a light source authentication algorithm based on the delay and sum of the light [...] Read more.
Indoor visible light positioning is susceptible to specular interference, which reduces the accuracy of positioning. From the perspective of increasing the number of identity documents (IDs), this paper proposes a light source authentication algorithm based on the delay and sum of the light source emission sequence. After obtaining the sub-light source emission sequence at the receiving end, the algorithm performs a cross-correlation operation on the adjacent sub-light source emission sequence in chiral order, and the emission sequence delay of the adjacent sub-light source can be obtained. Then, the relationship between the sum of the delays of all emitted sequences and the length of the sequence is calculated to identify the authenticity of the light source array. We conduct a simulation analysis of the light source authentication algorithm based on the delay and sum of the light source emission sequence. The results show that the algorithm can effectively improve the sequence utilization and localization success rate. Compared with the light source authentication algorithm based on the vector product, the number of light source IDs of the proposed algorithm is significantly increased. For example, when the number of light sources is 3 and the sequence length is 63, the number of light source IDs of the proposed algorithm is greater than that of the light source authentication algorithm based on the vector product by about 35. Therefore, the light source authentication algorithm based on the delay and sum of the light source emission sequence can effectively improve the utilization of the sequence. Full article
(This article belongs to the Special Issue Security and Privacy in AI and Large Model-Driven 6G Networks)
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