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A Review of Pseudonym Change Strategies for Location Privacy Preservation Schemes in Vehicular networks

ACM Computing Surveys 2025年第8期57卷 1-37页

作者： Benarous, Leila Zeadally, Sherali Boudjit, Saadi Mellouk, Abdelhamid Department of Computer science Amar Telidji University of Laghouat Laghouat Algeria LISSI-Tinc-NET Laboratory University of Paris-Est Creteil Créteil France College of Communication and Information University of Kentucky Lexington United States Department of Electronic Engineering Kyung Hee University Seoul Korea Republic of Litis Lab Intelligent Transportation Systems Group University of Rouen Normandy Mont-Saint-Aignan France Lissi Laboratory Department of Networks and Telecom University Paris-Est Créteil Val de Marne Créteil France

Location privacy protection in vehicular networks has been a primary priority to ensure because of its direct impact on human physical safety. Leakage and violation of road users' location privacy may be perilous beyond simple curiosity as it may scale to cyberstalking and tracking. This may lead to road users being subjected to mental and physical distress, from blackmailing to targeted advertising, and trap planning. The results of tracking may be fatal to road users. Over the past 17 years, researchers have been actively investigating location privacy protection mechanisms for vehicular networks. Numerous research results have been published that examined the use of pseudonym change strategies, which have been considered as the most appropriate solution that maintains the tradeoff between ease of application, good protection level, and the correct functionality of the network. We evaluate, classify, and analyze pseudonym change strategies aimed at location privacy preserving solutions for vehicular networks and discuss their performance. We also review the simulation tools used for evaluating pseudonym change techniques used in location privacy preserving solutions for the vehicular environment. The results of this survey will help researchers in this field to develop more robust, efficient, and cost-effective location privacy protection schemes in the future. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Differential privacy

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A High Performance Region Recognition Network-Enhanced Deep CNN for Image Content Perceptual Hashing

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IEEE Internet of Things Journal 2025年

作者： Yang, Meihong Qi, Baolin Ma, Bin Xu, Jian Xian, Yongjin Li, Xiaolong Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China School of Computer and Information Engineering Shandong University of Finance and Economics Jinan China Beijing Jiaotong University School of Computer and Information Technology Beijing100044 China

Perceptual image hashing has emerged as a crucial forensic tool within the Internet of Things (IoT) ecosystem. Traditional perceptual hashing algorithms predominantly rely on global image features to generate hash codes, which limit their ability to represent key features of images effectively. This paper introduces a Perceptual Region Recognition Network (PRRN) to accurately identify key feature regions in images based on their texture distribution characteristics, thereby generating image perceptual hashing codes that reflect the key content of the images. At the same time, a perceptual hashing feature extraction module, which integrates a Residual Network (ResNet) and a Weighted Feature Fusion Network (WFFN), is built to extract deep semantic features of the object image. Where, ResNet is leveraged to extract high-level semantic features, while WFFN ensures the preservation of low-level local features. Furthermore, skip connections are employed to achieve content enhancements for intricate details of critical image regions. Additionally, the Mean Squared Error (MSE) loss is incorporated to enhance the accuracy of key region localization, further improving the sensitivity of image perceptual hash codes and accelerating the network’s convergence speed. Extensive experimental evaluations demonstrate that the proposed PRRN-based perceptual image hashing scheme significantly outperforms other state-of-the-art methods in terms of image feature representation capability. Specifically, it achieves an average improvement of over 1.2% in attack-resistant capability for images compared with other counterparts, making it a promising candidate for practical applications in the IoT environment. © 2014 IEEE.

关键词： Mean square error

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FSSC: Federated Learning of Transformer Neural networks for Semantic Image Communication

FSSC: Federated Learning of Transformer Neural Networks for ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Yan, Yuna Zhang, Xin Li, Lixin Lin, Wensheng Li, Rui Cheng, Wenchi Han, Zhu Northwestern Polytechnical University School of Electronics and Information Xi'an710129 China Samsung Ai Center Cambridge United Kingdom Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States

ISBN: (纸本)9798350351255

In this paper, we address the problem of image semantic communication in a multi-user deployment scenario and propose a federated learning (FL) strategy for a Swin Transformer-based semantic communication system (FSSC). Firstly, we demonstrate that the adoption of a Swin Transformer for joint source-channel coding (JSCC) effectively extracts semantic information in the communication system. Next, the FL framework is introduced to collaboratively learn a global model by aggregating local model parameters, rather than directly sharing clients' data. This approach enhances user privacy protection and reduces the workload on the server or mobile edge. Simulation evaluations indicate that our method outperforms the typical JSCC algorithm and traditional separate-based communication algorithms. Particularly after integrating local semantics, the global aggregation model has further increased the Peak Signal-to-Noise Ratio (PSNR) by more than 2dB, thoroughly proving the effectiveness of our algorithm. © 2024 IEEE.

关键词： Federated learning

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Green UAV communications for 6G: A survey

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Chinese Journal of Aeronautics 2022年第9期35卷 19-34页

作者： Xu JIANG Min SHENG Nan ZHAO Chengwen XING Weidang LU Xianbin WANG School of Information and Communication Engineering Dalian University of TechnologyDalian 116024China State Key Laboratory of Integrated Service Networks Xidian UniversityXi'an 710071China School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China College of Information Engineering Zhejiang University of TechnologyHangzhou 310023China Department of Electrical and Computer Engineering Western UniversityLondon.ON N6A 5B9Canada

Unmanned Aerial Vehicles(UAVs) have received a wide range of attention for military and commercial applications. Enhanced with communication capability, UAVs are considered to play important roles in the Sixth Generation(6G) networks due to their low cost and flexible deployment. 6G is supposed to be an all-coverage network to provide ubiquitous connections for space, air, ground and underwater. UAVs are able to provide air-borne wireless coverage flexibly,serving as aerial base stations for ground users, as relays to connect isolated nodes, or as mobile users in cellular networks. However, the onboard energy of small UAVs is extremely limited. Thus,UAVs can be only deployed to establish wireless links temporarily. Prolonging the lifetime and developing green UAV communication with low power consumption becomes a critical *** this article, a comprehensive survey on green UAV communications for 6G is carried out. Specifically, the typical UAVs and their energy consumption models are introduced. Then, the typical trends of green UAV communications are provided. In addition, the typical applications of UAVs and their green designs are discussed. Finally, several promising techniques and open research issues are also pointed out.

关键词： Energy efficiency Green communications Sixth Generation(6G)networks Unmanned Aerial Vehicle(UAV) Wireless networks

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SJA: Server-driven Joint Adaptation of Loss and Bitrate for Multi-Party Realtime Video Streaming 42

SJA: Server-driven Joint Adaptation of Loss and Bitrate for ...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Shen, Kai Zhang, Dayou Zhu, Zi Zhang, Lei Wang, Fangxin Wang, Dan The Chinese University of HongKong The Future Network of Intelligence Institute Shenzhen China The Chinese University of HongKong School of Science and Engineering Shenzhen China Shenzhen University College of Computer Science and Software Engineering China Peng Cheng Laboratory China The Guangdong Provincial Key Laboratory of Future Networks of Intelligence China The Hong Kong Polytechnic University Department of Computing Hong Kong

ISBN: (纸本)9798350334142

The outbreak of COVID-19 has dramatically promoted the explosive proliferation of multi-party realtime video streaming (MRVS) services, represented by Zoom and Microsoft Teams. Different from Video-on-Demand (VoD) or live streaming, MRVS enables all-to-all realtime video communication, bringing significant challenges to service providing. First, unreliable network transmission can cause network loss, resulting in delay increase and visual quality degradation. Second, the transformation from two-party to multi-party communication makes resource scheduling much more difficult. Moreover, optimizing the overall QoE requires a global coordination, which is quite challenging given the various impact factors such as bitrate and *** this paper, we propose the SJA framework, which is, to our best knowledge, the first server-driven joint loss and bitrate adaptation framework in multi-party realtime video streaming services towards maximized QoE. We comprehensively design an appropriate QoE model for MRVS services to capture the interplay among perceptual quality, variations, bitrate mismatch, loss damage, and streaming delay. We mathematically formulate the QoE maximization problem in MRVS services. A Lyapunov-based relaxation and the SJA algorithm are further designed to address the optimization problem with close-to-optimal performance. Evaluations show that our framework can outperform the SOTA solutions by 18.4% ∼ 46.5%. © 2023 IEEE.

关键词： Quality control

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On-chip metamaterial-enabled high-order mode-division multiplexing

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Advanced Photonics 2023年第5期5卷 106-113页

作者： Yu He Xingfeng Li Yong Zhang Shaohua An Hongwei Wang Zhen Wang Haoshuo Chen Yetian Huang Hanzi Huang Nicolas KFontaine Roland Ryf Yuhan Du Lu Sun Xingchen Ji Xuhan Guo Yingxiong Song Qianwu Zhang Yikai Su Shanghai Jiao Tong University Department of Electronic EngineeringState Key Laboratory of Advanced Optical Communication Systems and NetworksShanghaiChina Nokia Bell Labs Murray HillNew JerseyUnited States Shanghai University Key Laboratory of Specialty Fiber Optics and Optical Access NetworksJoint International Research Laboratory of Specialty Fiber Optics and Advanced CommunicationShanghaiChina Shanghai Jiao Tong University School of Electronic Information and Electrical EngineeringJohn Hopcroft Center for Computer ScienceShanghaiChina

Mode-division multiplexing(MDM)technology enables high-bandwidth data transmission using orthogonal waveguide modes to construct parallel data ***,few demonstrations have been realized for generating and supporting high-order modes,mainly due to the intrinsic large material groupvelocity dispersion(GVD),which make it challenging to selectively couple different-order spatial *** show the feasibility of on-chip GVD engineering by introducing a gradient-index metamaterial structure,which enables a robust and fully scalable MDM *** demonstrate a record-high-order MDM device that supports TE_(0)–TE_(15)modes simultaneously.40-GBaud 16-ary quadrature amplitude modulation signals encoded on 16 mode channels contribute to a 2.162 Tbit∕s net data rate,which is the highest data rate ever reported for an on-chip single-wavelength *** method can effectively expand the number of channels provided by MDM technology and promote the emerging research fields with great demand for parallelism,such as high-capacity optical interconnects,high-dimensional quantum communications,and large-scale neural networks.

关键词： integrated photonics metamaterial mode-division multiplexing subwavelength grating

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Oblivious Encrypted Keyword Search with Fine-Grained Access Control for Cloud Storage

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5521-5533页

作者： Tong, Qiuyun Deng, Junyi Li, Xinghua Miao, Yinbin Wang, Yunwei Deng, Robert H. Xidian University State Key Laboratory of Integrated Services Networks and the School of Cyber Engineering Xi’an710071 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China Xidian University State Key Laboratory of Integrated Service Networks School of Cyber Engineering Xi’an710071 China Ministry of Education Engineering Research Center of Big data Security Xi’an710071 China Singapore Management University School of Information Systems Singapore

With the rapid expansion of data volumes in cloud computing, more data owners are opting to outsource their data to cloud service providers to reduce local storage and management costs. However, data outsourcing deprives data owners of direct physical control over their data, increasing the risk of unauthorized access and exposure of sensitive information. To mitigate these risks, various privacy-preserving keyword search schemes with access control have been developed, but many are vulnerable to leakage-abuse attacks due to the exposure of access, search or volume patterns, which can lead to privacy breaches in outsourced data and queries. To solve this problem, we propose an oblivious encrypted keyword search scheme with fine-grained access control, called OEKA. It enables efficient oblivious keyword search over encrypted multi-maps by using the adapted XOR filter and distributed point function, ensuring protection of access, search and volume patterns. Moreover, OEKA enforces role-based access control by using polynomial-based access strategy and keyword-based private information retrieval, allowing access policies of retrieved objects to be detecting without revealing the objects themselves. A formal security analysis verifies the scheme’s robustness, and experimental results demonstrate its practical efficiency. 2005-2012 IEEE.

关键词： Authorization

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Use a Little Force to Move a Great Mass: A Jamming Leverage Strategy for Covert Communications

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5015-5024页

作者： Wang, Tingting Li, Zan Si, Jiangbo Cheng, Zihao Gao, Yang Al-Dhahir, Naofal Xidian University Integrated Service Networks Laboratory Xi’an710126 China Air Force Engineering University Information and Navigation College Xi’an710043 China The University of Texas at Dallas Department of Electrical and Computer Engineering RichardsonTX75080 United States

We propose a joint covert beamforming design and jamming strategy to protect the communication process between Alice and Bob from being discovered by Willie with the help of another pair of neutral nodes. Specifically, with the help of irrelevant communication parties that commonly exist in practical communication scenarios, Jammer increases his transmission power by interfering with the neutral receiver, thus indirectly increasing the interference to Willie, which can be viewed as leveraging the force to make a big impact with a small effect. In our designed beamformer, we jointly optimize the beam power allocation factor, Alice’s transmission power, and Jammer’s transmission power when Alice transmits, to maximize the covert rate, which also maximizes Alice’s transmission power. In the perfect channel state information (CSI) scenario, the transformed optimization problem is solved via a one-dimensional search method and CVX solver. Due to the solution’s high complexity, we further propose a method to determine the optimal power allocation factor. For the imperfect Willie’s CSI scenario, three cases are investigated: Alice to Willie imperfect CSI, Transmitter to Willie imperfect CSI, and Jammer to Willie imperfect CSI. We utilize the S-procedure to tackle the optimization problem. Simulation results demonstrate the effectiveness of our proposed strategy. © 2005-2012 IEEE.

关键词： Beamforming

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LLM-Assisted Reinforcement Learning: Leveraging Lightweight Large Language Model Capabilities for Efficient Task Scheduling in Multi-Cloud Environment

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IEEE Transactions on Consumer Electronics 2024年

作者： Tang, Xuhao Liu, Fagui Xu, Dishi Jiang, Jun Tang, Quan Wang, Bin Wu, Qingbo Philip Chen, C.L. South China University of Technology School of Computer Science and Engineering Guangzhou510006 China Pengcheng Laboratory Department of New Networks Shenzhen518066 China National University of Defense Technology College of Computer Changsha410073 China

In the contemporary landscape of large language models (LLMs) development, it is crucial to address the challenges of deploying these models on hardware-constrained consumer electronic devices (CEDs), especially within complex dynamic task scheduling in multi-cloud environments (MCE). We propose a novel methodology leveraging a lightweight LLM to enhance task scheduling decisions in *** approach involves creating a task scheduling expert database informed by optimization objectives to fine-tune the lightweight LLM. This enables the model to generate a schedulable candidate set of tasks based on the current state of tasks and operational conditions within CEDs across MCE, optimizing scheduling decisions and enhancing overall efficiency. Simulations using both synthetic and real-world datasets demonstrate that our method outperforms three other algorithms in cost minimization, makespan reduction, and energy consumption. In summary, our methodology empowers CEDs to optimize the utilization of multi-cloud resources and harness the capabilities of lightweight LLMs to effectively minimize makespan, operational costs, and energy consumption during the task scheduling process, thereby facilitating efficient task scheduling. © 1975-2011 IEEE.

关键词： Deep reinforcement learning

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Covert Communications for Cognitive Satellite Terrestrial networks

Covert Communications for Cognitive Satellite Terrestrial Ne...

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IEEE Conference on Wireless Communications and Networking

作者： Pengpeng Liu Jiangbo Si Zan Li Naofal Al-Dhahirl State Key Laboratory of Integrated Services Networks Xidian University Xi'an China Department of Electrical and Computer Engineering The University of Texas at Dallas Richardson USA

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

This work investigates a cognitive satellite-terrestrial covert communication network, where a satellite serves as the primary transmitter to communicate with a primary user (PU) covertly under the surveillance of an unauthorized warden and a terrestrial base station (BS) serves as the secondary transmitter to communicate with a secondary user. Considering imperfect channel state information (CSI) of warden, we jointly design both satellite's transmit power and BS's transmit beamforming to maximize the PU's covert rate while satisfying the terrestrial network's information rate requirement, satellite network's covertness and both satellite's and BS's power constraints. The successive convex approximation method is employed to obtain the optimal BS beamforming. In addition, the imperfect CSI is tackled by the $S$ -procedure technique. Simulation results reveal that compared to other benchmark schemes the proposed optimization algorithm performs better in terms of the covert rate.

关键词： Satellites Transmitters Array signal processing Simulation Surveillance Benchmark testing Space-air-ground integrated networks

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