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A secure quantum homomorphic encryption ciphertext retrieval scheme

Soft Computing 2025年第3期29卷 1497-1509页

作者： Cheng, Zhen-Wen Chen, Xiu-Bo Xu, Gang Chang, Yan Miao, Li-Hua Yang, Yi-Xian Wang, Ya-Lan School of Data and Computer Science Shandong Women’s University Jinan250300 China Information Security Center State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China School of Information Science and Technology North China University of Technology Beijing100144 China School of Cybersecurity Chengdu University of Information Technology Chengdu610225 China Huawei Technologies Co. Ltd Shenzhen518129 China Department of Computer Science Faculty of Engineering and Physical Sciences University of Surrey Surrey GuildfordGU2 7XH United Kingdom

In a recent paper (Gong et al. Quantum Inf Process 19:3, 2020), a novel ciphertext retrieval scheme based on the Grover algorithm and quantum homomorphic encryption was presented. In this scheme, when the server performs the operation of marking the solution on the user’s encrypted state in the Grover iteration, it needs to remove many gate-errors generated in the homomorphic evaluation of the T gate. And the server could judge this specific solution from the quantum circuit of marking the solution. It makes this scheme unable to achieve the low-cost and secure ciphertext retrieval. Therefore, we improve the Gong et al.’s scheme and propose a secure quantum homomorphic encryption ciphertext retrieval scheme. In our scheme, the trusted third party is introduced to cooperate with the server to execute the Grover algorithm. In each Grover iteration, the trusted third party can quickly mark the solution on the plaintext state, encrypt the marked state, and transmit it to the server. Then the server performs the remaining operations of this Grover iteration on the encrypted state. The trusted third party finally decrypts the iterated state. This cooperative approach ensures that the number of auxiliary qubits required and extra quantum gates executed in our scheme are lower than the Gong et al.’s scheme. By analyzing the security of our scheme, we confirm that the server and the trusted third party will not be informed of this solution. Thus, our scheme realizes the secure ciphertext retrieval with low computational overhead. We utilize IBM’s Qiskit framework to simulate our scheme, and the experimental result shows that our scheme is correct. It is worth noting that the low-cost and secure ciphertext retrieval will play a crucial role in modern information security and privacy protection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Ciphertext

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Unmasking Digital Falsehoods: A Comparative Analysis of LLM-Based Misinformation Detection Strategies

arXiv

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arXiv 2025年

作者： Huang, Tianyi Yi, Jingyuan Yu, Peiyang Xu, Xiaochuan Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA United States The Information Networking Institute Carnegie Mellon University PittsburghPA United States

The proliferation of misinformation on social media has raised significant societal concerns, necessitating robust detection mechanisms. Large Language Models such as GPT-4 and LLaMA2 have been envisioned as possible tools for detecting misinformation based on their advanced natural language understanding and reasoning capabilities. This paper conducts a comparison of LLM-based approaches to detecting misinformation between text-based, multimodal, and agentic approaches. We evaluate the effectiveness of fine-tuned models, zero-shot learning, and systematic fact-checking mechanisms in detecting misinformation across different topic domains like public health, politics, and finance. We also discuss scalability, generalizability, and explainability of the models and recognize key challenges such as hallucination, adversarial attacks on misinformation, and computational resources. Our findings point towards the importance of hybrid approaches that pair structured verification protocols with adaptive learning techniques to enhance detection accuracy and explainability. The paper closes by suggesting potential avenues of future work, including real-time tracking of misinformation, federated learning, and cross-platform detection models. © 2025, CC BY.

关键词： Zero-shot learning

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A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit

arXiv

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arXiv 2025年

作者： Huang, Tianyi Xu, Zeqiu Yu, Peiyang Yi, Jingyuan Xu, Xiaochuan Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Information Networking Institute Carnegie Mellon University PittsburghPA15213 United States

In this paper, we propose an optimized Transformer model that integrates Bayesian algorithms with a Bidirectional Gated Recurrent Unit (BiGRU), and apply it to fake news classification for the first time. First, we employ the TF-IDF method to extract features from news texts and transform them into numeric representations to facilitate subsequent machine learning tasks. Two sets of experiments are then conducted for fake news detection and classification: one using a Transformer model optimized only with BiGRU, and the other incorporating Bayesian algorithms into the BiGRU-based Transformer. Experimental results show that the BiGRU-optimized Transformer achieves 100% accuracy on the training set and 99.67% on the test set, while the addition of the Bayesian algorithm maintains 100% accuracy on the training set and slightly improves test-set accuracy to 99.73%. This indicates that the Bayesian algorithm boosts model accuracy by 0.06%, further enhancing the detection capability for fake news. Moreover, the proposed algorithm converges rapidly at around the 10th training epoch with accuracy nearing 100%, demonstrating both its effectiveness and its fast classification ability. Overall, the optimized Transformer model, enhanced by the Bayesian algorithm and BiGRU, exhibits excellent continuous learning and detection performance, offering a robust technical means to combat the spread of fake news in the current era of information overload. © 2025, CC BY.

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Satellite Selection for In-Band Coexistence of Dense LEO Networks

arXiv

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arXiv 2025年

作者： Kim, Eunsun Roberts, Ian P. Lee, Taekyun Andrews, Jeffrey G. 6G@UT Research Center Wireless Networking and Communications Group University of Texas Austin United States Wireless Lab Department of Electrical and Computer Engineering UCLA United States

We study spectrum sharing between two dense low-earth orbit (LEO) satellite constellations, an incumbent primary system and a secondary system that must respect interference protection constraints on the primary system. In particular, we propose a secondary satellite selection framework and algorithm that maximizes capacity while guaranteeing that the time-average interference and absolute interference inflicted upon each primary ground user never exceeds specified thresholds. We solve this NP-hard constrained, combinatorial satellite selection problem through Lagrangian relaxation to decompose it into simpler problems which can then be solved through subgradient methods. A high-fidelity simulation is developed based on public FCC filings and technical specifications of the Starlink and Kuiper systems. We use this case study to illustrate the effectiveness of our approach and that explicit protection is indeed necessary for healthy coexistence. We further demonstrate that deep learning models can be used to predict the primary satellite system associations, which helps the secondary system avoid inflicting excessive interference and maximize its own capacity. Copyright © 2025, The Authors. All rights reserved.

关键词： Orbits

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Knowledge Retrieval Based on Generative AI

arXiv

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arXiv 2025年

作者： Yang, Te-Lun Liu, Jyi-Shane Tseng, Yuen-Hsien Jang, Jyh-Shing Roger Graduate Institute of Networking and Multimedia National Taiwan University Taiwan Department of Computer Science National Chengchi University Taiwan Graduate Institute of Library & Information Studies National Taiwan Normal University Taiwan Department of Computer Science & Information Engineering National Taiwan University Taiwan

This study develops a question-answering system based on Retrieval-Augmented Generation (RAG) using Chinese Wikipedia and Lawbank as retrieval sources. Using TTQA and TMMLU+ as evaluation datasets, the system employs BGEM3 for dense vector retrieval to obtain highly relevant search results and BGE-reranker to reorder these results based on query relevance. The most pertinent retrieval outcomes serve as reference knowledge for a Large Language Model (LLM), enhancing its ability to answer questions and establishing a knowledge retrieval system grounded in generative AI. The system's effectiveness is assessed through a two-stage evaluation: automatic and assisted performance evaluations. The automatic evaluation calculates accuracy by comparing the model's auto-generated labels with ground truth answers, measuring performance under standardized conditions without human intervention. The assisted performance evaluation involves 20 finance-related multiple-choice questions answered by 20 participants without financial backgrounds. Initially, participants answer independently. Later, they receive system-generated reference information to assist in answering, examining whether the system improves accuracy when assistance is provided. The main contributions of this research are: (1) Enhanced LLM Capability: By integrating BGE-M3 and BGE-reranker, the system retrieves and reorders highly relevant results, reduces hallucinations, and dynamically accesses authorized or public knowledge sources. (2) Improved Data Privacy: A customized RAG architecture enables local operation of the LLM, eliminating the need to send private data to external servers. This approach enhances data security, reduces reliance on commercial services, lowers operational costs, and mitigates privacy risks. © 2025, CC BY.

关键词： Question answering

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Multi-modal fusion in thermal imaging and MRI for early cancer detection

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Journal of Thermal Biology 2025年 129卷 104090页

作者： Hsu, Ching-Hsien Pandeeswaran, C. Jesi V, Elizabeth Thilahar C, Rajendra Department of Computer Science and Information Engineering Asia University Taiwan Department of Medical Research China Medical University Hospital China Medical University Taiwan Computer Science Engineering Department St.Joseph's College of Engineering OMR Chennai 600 119 India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur India Department of Computer Science and Engineering Saveetha Engineering College Chennai India

Early detection of cancer relies on precise imaging that captures both structural and metabolic details, critical for identifying small yet significant tissue anomalies. Thermal imaging detects temperature changes linked to increased metabolic activity in cancerous tissues, while Magnetic Resonance Imaging (MRI) provides high-resolution soft tissue contrast. However, traditional single-modality imaging techniques can lack sufficient sensitivity or anatomical context when used alone. To address this, we propose a novel end-to-end unsupervised Multi-Mode Fused Recursive Neural Network (MMF-RvNN) framework modified to fuse thermal imaging with MRI, generating high-quality fused images that capture both thermal and MRI anatomical breast cancer information to enhance early-stage breast cancer detection. The breast cancer thermal and MRI images were subjected to a non-rigid registration process using anatomical landmarks to align both modalities accurately. The preprocessing steps include min-max normalization, bilinear interpolation, and Non-Local Means (NLM) methods are applied to normalize intensity values, adjust pixel size, and reduce noise in the scanning images. The multi-resolution analysis process involves using Static Curvelet Transform (SCLT) to decompose images into various frequency bands for feature extraction. Fusion techniques include the integration of SCLT and t-distributed Stochastic Neighbor Embedding (t-SNE) for low-frequency and high-frequency fusion, respectively. The novel MMF-RvNN generates fused output images by training a generator and evaluating their realism. Loss functions include content, style, and L1 losses to preserve texture and structural information. The result demonstrates the proposed model achieves a high rate of structural similarity index measure (SSIM) is 0.98, peak signal-to-noise ratio (PSNR) is 45.9 dB, and entropy (ENT) is 8.7 then decreasing the High-Frequency Error Norm (HFEN) by 0.019 and Normalized Mean Square Error (NMSE

关键词： Cancer tumors and early state detection Magnetic resonance imaging (MRI) Multi-mode fused recursive neural network (MMF-RvNN) Thermal imaging

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Improving Data Encoding for Enhanced AI Performance in Complex Datasets via Quantum Feature Space Mapping: Harnessing Quantum Algorithms

Improving Data Encoding for Enhanced AI Performance in Compl...

引用

Pervasive Computational Technologies (ICPCT), International Conference on

作者： Gurpreet Singh Walia Sathiya Priya S KV Karthikeya D. Suresh P Sudheer V. Syambabu Lead Enterprise Security and Networking Architect NetApp Inc. Durham NC Department of ECE Panimalar Engineering College Chennai AT&T Sattva Knowledge City Hyderabad Department of ECE St. Joseph's Institute of Technology Chennai Department of Computer Science and Engineering (AI&ML) CVR College of Engineering Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

The purpose of this study is to investigate the potential for quantum feature space mapping to be utilized in the enhancement of data encoding approaches for artificial intelligence (AI) systems that are capable of managing complicated datasets. Traditional artificial intelligence models frequently face challenges when confronted with the high-dimensionality and non-linearity of such datasets. This can hinder their performance in areas such as pattern recognition, optimization, and predictive modeling. Quantum computing, which is able to make use of quantum superposition and entanglement, provides a method that is revolutionary in nature by means of quantum feature space mapping. By encoding data into quantum states, this technology enables the construction of feature spaces that are tenfold bigger than those that would be possible in classical conditions. This makes it much simpler to differentiate between intricate patterns that would otherwise be indistinguishable. The purpose of this research is to investigate the application of quantum algorithms, such as Quantum Support Vector Machines (QSVM) and Variational Quantum Classifiers (VQC), with the intention of enhancing the capacity of artificial intelligence to learn from complex and high-dimensional data. We demonstrate that quantum feature space mapping leads to more efficient and accurate data classification, regression, and clustering tasks by comparing the performance of quantum-enhanced artificial intelligence models to that of their classical counterparts on difficult datasets. Additionally, the research addresses important difficulties, such as the constraints of the quantum hardware that is now available and the requirement for quantum circuits that are scalable. Quantum computing has the potential to improve the performance of artificial intelligence in the management of complicated, high-dimensional data, which may ultimately pave the way for advances in domains such as genetics, finance, and cryptogra

关键词： Support vector machines Quantum computing Quantum algorithm Quantum entanglement Computational modeling Space mapping Quantum state Encoding Hardware Quantum circuit

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Plant Leaf Disease Detection Using IoT and Deep Learning Approach

Plant Leaf Disease Detection Using IoT and Deep Learning App...

引用

Cognitive Computing in engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： N. Senthamarai R. Srinivasan M. Kavitha R. Kavitha Networking and Communications SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

Modern detecting innovations, with a specific spotlight on manageability related issues, are impacting the fate of farming as the globe turns out to be more interconnected. The majority of emerging countries' economies is based on agriculture. The agricultural industry is one of the most vital industries, and crops are a necessity for providing food to people all over the world. The agricultural sector depends heavily on early disease detection and recognition. Crop fields are susceptible to the outbreak of plant diseases, either as a result of spreading viruses and fungus or unfavorable weather conditions, due to the lack of trained labor and enough supporting infrastructure. Owners of community gardens that want to use affordable strategies for sustainable agriculture through a sustainable. This model's suggestion offers a practical means of identifying various ailments in a range of plant species. Using the recently created dataset, an improved variant of the best deep learning (DL) model, has been suggested. In this unique circumstance, think about the possible utilization of the Social Internet of Things for detecting and interchanges, deep learning for the identification of plant sicknesses, and crowd sourcing for the collection and grouping of pictures, drawing in ranchers and local area garden proprietors and specialists. Across data fusion and deep learning, the created structure can take utility of the information gathered and estimate with a certain level of accuracy when a plant would (or would not) contract a disease. The user application must be able to interface with the virtualization layer and must be able to assess the health of the plants using images, data from garden sensors, and Internet access. Take some photos of the plant to assess its health and to provide feedback on any photos of the plant that haven't yet been tagged for the classifier data set. In addition to the user's smart phone's photo, the diagnosis of a crop disease necessitates c

关键词： Deep learning Plant diseases Technological innovation Accuracy Crops Agriculture Social Internet of Things Virtualization Intelligent sensors Tuning

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Multi-Agent Systems for Autonomous IoT Network Management Using Distributed Reinforcement Learning

Multi-Agent Systems for Autonomous IoT Network Management Us...

引用

International Symposium on Advanced Computing and Communication (ISACC)

作者： Neelamegam G Rajaram Venkatesan Ramya SR R.S. Ramya Akshya. J M. Sundarrajan Mani Deepak Choudhry Department of Computer Science and Engineering VSB College of Engineering and Technology Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data Science Builders Engineering College Tirupur Tamil Nadu India Department of Computer Science and Engineering Dr NGP Institute of Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India Department of Computational Intelligence SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Networking and Communications SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

The ever-growing complexity of IoT networks ignited by their wide scale adoption in applications such as smart cities, the industrial automation, and health care, compelled to develop sophisticated yet resource efficient and scalable network management solution. Centralized systems are traditional in architecture, which leads to higher latency and bottlenecks, while decentralized systems can distribute workloads however the lack of adaptability to real time, along with the difficulty of extracting maximum resource usage, are limitations. In recent years, reinforcement learning (RL) based approaches have taken shape as a promising alternative to facilitate the automated learning and optimisation of task management. However, there are two challenges for existing RL based methods: slow convergence, inefficient priority assignment of tasks, and poor coordination amongst agents in the case of dynamic, large scale IoT environment. To solve these challenges, in this research we propose a distributed reinforcement learning based multi agent framework for managing IoT network. The framework also integrates a task prioritization mechanism to dynamically choose an optimal task allocation solution. Further validation was performed in a simulated IoT environment where real world inspired datasets were used. The proposed framework demonstrated 30% more task throughput than centralized, 25% less latency than decentralized, and 40% more energy efficiency than current RL based systems. The system was further validated using advanced visualizations such as anomaly detection maps and resource allocation efficiency graphs, demonstrating its capacity to manage dynamic load of tasks and resource allocation. The proposed framework addresses the critical limitations of current methods, through scalable, adaptive, and energy efficient framework for managing IoT network, and accordingly helps advancing the intelligent and autonomous IoT systems.

关键词： Smart cities Reinforcement learning Medical services Throughput Dynamic scheduling Energy efficiency Real-time systems Resource management Optimization Multi-agent systems

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A Survey of Integrating Generative Artificial Intelligence and 6G Mobile Services: Architectures, Solutions, Technologies and Outlooks

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IEEE Transactions on Cognitive Communications and networking 2025年

作者： Liu, Yi-Jing Du, Hongyang Xu, Xinyi Zhang, Ruichen Feng, Gang Cao, Bin Niyato, Dusit Kim, Dong In Jamalipour, Abbas Letaief, Khaled B. Tafazolli, Rahim University of Electronic Science and Technology of China National Key Lab on Wireless Communications Chengdu China University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Nanyang Technological University College of Computing and Data Science Singapore Singapore Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of University of Sydney School of Electrical and Computer Engineering Sydney Australia Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong University of Surrey Institute for Communication Systems School of Computer Science and Electronic Engineering United Kingdom

Generative artificial intelligence (GenAI) is rapidly driving a new phase of artificial intelligence revolution, marked by various applications such as ChatGPT, Sora and DeepSeek. With powerful capabilities in content generation, personalization and user interaction, GenAI can drive significant enhancements in various mobile services to satisfy individual user preferences. Meanwhile, mobile services offer substantial support for accessibility, functionality and intelligence of GenAI applications. However, current research on integrating GenAI and mobile services remains nascent, lacking necessary theoretical and technical support. In this survey, we break down a new vision to guide research on integrating GenAI and mobile services, aiming to improve 6G intelligence levels and enable 6G mobile networks to support the integration of GenAI and mobile services effectively. We begin by examining the necessity of integrating GenAI with 6G mobile services and exploring potential learning architectures of GenAI models within mobile networks. Next, we analyze potential solutions from two perspectives: how 6G mobile networks empower GenAI and how GenAI enables mobile services. We subsequently explore a promising use case, along with essential features and techniques, to facilitate the integration of GenAI and mobile services. Finally, we discuss future research directions, aiming at realizing intelligent 6G networks. © 2015 IEEE.

关键词： Generative adversarial networks

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