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PriChain: Efficient Privacy-Preserving Fine-Grained Redactable Blockchains in Decentralized Settings

Chinese Journal of Electronics 2025年第1期34卷 82-97页

作者： Hongchen Guo Weilin Gan Mingyang Zhao Chuan Zhang Tong Wu Liehuang Zhu Jingfeng Xue School of Computer Science and Technology Beijing Institute of Technology School of Cyberspace Science and Technology Beijing Institute of Technology School of Computer and Communication Engineering University of Science and Technology Beijing

Recently, redactable blockchain has been proposed and leveraged in a wide range of real systems for its unique properties of decentralization, traceability, and transparency while ensuring controllable on-chain data redaction. However, the development of redactable blockchain is now obstructed by three limitations, which are data privacy breaches, high communication overhead, and low searching efficiency, respectively. In this paper, we propose PriChain, the first efficient privacy-preserving fine-grained redactable blockchain in decentralized settings. PriChain provides data owners with rights to control who can read and redact on-chain data while maintaining downward compatibility, ensuring the one who can redact will be able to read. Specifically, inspired by the concept of multi-authority attribute-based encryption, we utilize the isomorphism of the access control tree, realizing fine-grained redaction mechanism, downward compatibility, and collusion resistance. With the newly designed structure, PriChain can realize O（n） communication and storage overhead compared to prior O（n2） schemes. Furthermore, we integrate multiple access trees into a tree-based dictionary, optimizing searching efficiency. Theoretical analysis proves that PriChain is secure against the chosen-plaintext attack and has competitive complexity. The experimental evaluations show that PriChain realizes 10× efficiency improvement of searching and 100× lower communication and storage overhead on average compared with existing schemes.

关键词： Resistance Access control Data privacy Dictionaries Blockchains Encryption Complexity theory Usability Protection

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Sentiment analysis algorithm based on word embedding in text mining

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International Journal of Speech Technology 2025年第1期28卷 141-151页

作者： Hu, Qiong School of Computer and Communication Engineering Nanjing Tech University Pujiang Institute Nanjing210000 China

This study proposes a sentiment analysis algorithm model that combines word embedding and machine learning algorithms to address issues such as data shortage, low accuracy, and limited coverage. The proposed model uses the continuous bag-of-words word embedding method for word analysis and incorporates convolutional neural network, long and short-term memory neural network model, and attention mechanism to enhance the traditional word embedding *** research results obtained using the English review dataset and the hotel review dataset, which contain an average of 10,000 entries, show that in the performance comparison of hotel review sentiment analysis tests, the highest value of the model loss function used in this study is 0.92. The accuracy, recall, and F1 value of the model are 90.56%, 91.65%, and 92.36%, respectively, which are 5.89%, 7.00%, and 6.74% higher than the lowest values in the convolutional neural network, long short term memory neural network, and convolutional long short term memory neural network. In the performance comparison for different emotional tendencies, the algorithm model used in this study achieved various indicator values of 94.68%, 93.86%, and 94.68% in sentiment analysis, respectively. The model with attention mechanism has a higher accuracy, with an accuracy of 96.4%. This shows that the algorithmic model used in the study is able to improve the accuracy and effectiveness of sentiment analysis to some extent. The research model is particularly suitable for sentiment analysis tasks in scenarios such as product reviews and hotel evaluations on e-commerce platforms, and can extract key emotional information from massive user reviews. This provides strong support for user sentiment analysis and data-driven decision-making in industries such as e-commerce and tourism. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Accuracy Attention mechanisms Convolutional neural networks Long short term memory neural network Sentiment analysis Text mining

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Intelligent integrated sensing and communication:a survey

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Science China(Information Sciences) 2025年第3期68卷 5-46页

作者： Jifa ZHANG Weidang LU Chengwen XING Nan ZHAO Naofal AL-DHAHIR George K.KARAGIANNIDIS Xiaoniu YANG School of Information and Communication Engineering Dalian University of Technology College of Information Engineering Zhejiang University of Technology School of Information and Electronics Beijing Institute of Technology Department of Electrical and Computer Engineering The University of Texas at Dallas Department of Electrical and Computer Engineering Aristotle University of Thessaloniki

Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. However, the traditional ISAC schemes are highly dependent on the accurate mathematical model and suffer from the challenges of high complexity and poor performance in practical scenarios. Recently, artificial intelligence (AI) has emerged as a viable technique to address these issues due to its powerful learning capabilities, satisfactory generalization capability, fast inference speed, and high adaptability for dynamic environments, facilitating a system design shift from model-driven to data-driven. Intelligent ISAC, which integrates AI into ISAC, has been a hot topic that has attracted many researchers to investigate. In this paper, we provide a comprehensive overview of intelligent ISAC, including its motivation, typical applications, recent trends, and challenges. In particular, we first introduce the basic principle of ISAC, followed by its key techniques. Then, an overview of AI and a comparison between model-based and AI-based methods for ISAC are provided. Furthermore, the typical applications of AI in ISAC and the recent trends for AI-enabled ISAC are reviewed. Finally, the future research issues and challenges of intelligent ISAC are discussed.

关键词： artificial intelligence deep learning deep reinforcement learning federated learning generative artificial intelligence integrated sensing and communication machine learning transfer learning

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Verification of news and certificate, and plagiarism detector

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Multimedia Tools and Applications 2025年 1-28页

作者： Jain, Pushpak Anand, Dewansh Rajni Behal, Vansh Kapoor, Vipul Sharma, Neha Jindal, Neeru Department of Electronics and Communication Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab Patiala India

Fake news, Fake certification, and Plagiarism are the most common issues arising these days. During this COVID-19 situation, there are a lot of rumors and fake news spreading and some of us are using fake certification to avoid vaccination and plagiarism became the most common topic during online studies. It is found that several apps and websites are present for the detection of false news and plagiarism. However, there is no single website for the detection of fake certificates. Moreover, nothing is made that consists of all these features in one platform. So, this proposed work develops a website for detecting fake news, Fake certificate, and plagiarism detector. The proposed method uses Natural Language Processing techniques for detecting NPC (news, plagiarism, and certificate). The proposed work uses image processing with some Python libraries like OpenCV in all the detections and that makes it quite interesting. For detecting NPC (news, plagiarism, and certificate) the algorithm is built using a k-mean model, logistic regression, and neural network. The proposed work uses the datasets that are trained using the count vectorizer method for the detection and to get its accuracy which will be tested using machine learning algorithms. The proposed method has shown an accuracy rate of 94.28% on Logistics Regression in comparison to existing methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Fake news Machine learning Natural language processing Plagiarism Social media platform

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Harnessing Machine Learning in Dynamic Thermal Management in Embedded CPU-GPU Platforms

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ACM Transactions on Design Automation of Electronic Systems 2025年第2期30卷 1-32页

作者： Maity, Srijeeta Majumder, Anirban Roy, Rudrajyoti Hota, Ashish Dey, Soumyajit Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India Electrical Engineering Indian Institute of Technology Kharagpur Kharagpur India Electronics And Communication Engineering Indian Institute of Technology Kharagpur Kharagpur India

With increasing transistor density, modern heterogeneous embedded processors often exhibit high temperature gradients due to complex application scheduling scenarios which may have missed design considerations. In many use cases, off-chip "active"cooling solutions are considered prohibitive in such reduced form factors. Core frequency throttling by existing dynamic thermal management techniques often compromises the Quality-of-Service (QoS) and violates real-time deadlines. This necessitates the adoption of intelligent resource management that simultaneously manages both thermal and latency performance. Coupled with the complexity of modern heterogeneous multi-cores, the periodic application updates that cater to ever-changing user requirements often render model-driven thermal-aware resource allocation approaches unsuitable for heterogeneous multi-core systems. For such application-architecture scenarios, we propose a novel self-learning based resource manager using Reinforcement Learning that intelligently manipulates core frequencies and task set mappings to fulfill thermal and latency objectives. Our framework employs a data-driven system modeling technique using Gaussian Process Regression to enable efficient offline training of this learning-based resource manager to avoid challenges associated with initial online training. We evaluate the approach on a heterogeneous embedded CPU-GPU platform with real workloads and observe a significant reduction in peak operating temperature when compared to the default onboard frequency governor as well as other learning-based state-of-the-art approaches. © 2025 Copyright held by the owner/author(s).

关键词： Quality of service

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"Survey and analysis of blockchain technologies with respect to: properties, algorithms, architecture, models, evolution and framework"

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Multimedia Tools and Applications 2025年第14期84卷 13571-13615页

作者： Tiwari, Nikita Ranjan, Prakash Biswal, Pradeep Kumar Barde, Chetan Sinha, Neha Department of Computer Science and Engineering Indian Institute of Information Technology Bhagalpur Bhagalpur India Department of Electronics and Communication Engineering Indian Institute of Information Technology Bhagalpur Bhagalpur India

The growing realm of blockchain technology has captivated researchers and practitioners alike with its promise of decentralized, secure, and transparent transactions. This paper presents a comprehensive survey and analysis of various blockchain technologies, focusing on their fundamental properties, architectural designs, operational models, evolutionary trends, and overarching frameworks. Through an extensive review of existing literature and empirical studies, this research synthesizes the multifaceted landscape of blockchain, elucidating key concepts such as consensus mechanisms, smart contracts, scalability, interoperability, and privacy protocols. By examining the evolution of blockchain from its inception to its current state and beyond, this paper delineates the trajectory of technological advancements and identifies emerging paradigms. Furthermore, it critically evaluates the strengths and limitations of diverse blockchain architectures and models, offering insights into their applicability across different domains and use cases. Finally, this study culminates in the proposal of a comprehensive framework that encapsulates the essential elements necessary for understanding, implementing, and harnessing the transformative potential of blockchain technologies. Through this holistic analysis, this research contributes to a deeper understanding of blockchain ecosystems and provides valuable guidance for future research directions and practical implementations. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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Research on Clothing Recommendation Technology Integrating Multimodal Data 24

Research on Clothing Recommendation Technology Integrating M...

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2nd International Conference on Artificial Intelligence, Systems and Network Security, AISNS 2024

作者： Lin, Rong He, Yi Tang, Jiale Tang, Zhihang School of Computer and Communication Hunan Institute of Engineering Hunan Xiangtan China

ISBN: (纸本)9798400711237

Due to the rapid increase in the volume of text data, traditional recommendation systems that rely solely on textual information are struggling to keep up with the evolving fashion trends and the personalized needs of users. In today’s age of information overload, users are no longer satisfied with simple text descriptions when making clothing choices. They seek a comprehensive evaluation of information through various means such as text descriptions, images, and user comments. This paper introduces a clothing recommendation algorithm that utilizes a multimodal data mining technique, integrating it with a multi-vector semantic segmentation method for a clothing product recommendation system. By conducting in-depth multimodal data analysis and introducing topic words, the algorithm generates personalized recommendations that align with users’ needs. The experimental results demonstrate the algorithm’s effectiveness in reducing confusion between different topics. With the LOP parameter set to 20, the Precision and MAP values of the model proposed in this paper are better than the comparison model. This algorithm can effectively mine potential information within multimodal data, identify users’ potential points of interest, and accurately predict user preferences, boosting merchandise sales and user growth on the apparel e-commerce platform. © 2024 Copyright held by the owner/author(s).

关键词： Semantic Segmentation

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A Coherent Analytical Model to Strengthen Vehicular communication in VANET Using Droptail Priority Queue

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Journal of The Institution of Engineers (India): Series B 2025年 1-12页

作者： Kumar, Satyam Sinha, Bam Bahadur Khare, Priyank Electronics and Communication Engineering Indian Institute of Information Technology Ranchi Jharkhand Ranchi834004 India Computer Science and Engineering National Institute of Technology Sikkim Sikkim Ravangla737139 India

People’s demand for vehicles has been increasing day by day over the last few decades. A survey tells us that over 50,000 vehicles run on the roads per day. Such a large number of vehicles causes traffic. A survey tells us that over 1200 road crashes occur everyday in India out of which 25% result in deaths. Most of these accidents occur due to a lack of communication between the vehicles. This has led to the attraction of researchers towards the Vehicular Ad Hoc Network (VANET) ensuring road safety by establishing an Intelligent Transportation System (Malhi in Comput Secur 89:101664, 2020). In VANET, due to the frequent movement of vehicles and rapid variations in topology, the communication link between vehicles may break. This paper analyzes these problems and comes up with a novel approach to overcome these and maintain the performance of the protocol. In order to assess the performance metrics for the proposed protocol, an analytical model has been created. Simulations have been carried out to validate it and have been compared against similar state of comparative methods. © The Institution of Engineers (India) 2025.

关键词： Vehicular ad hoc networks

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On the Price of Decentralization in Decentralized Detection

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IEEE Transactions on Information Theory 2025年第4期71卷 2341-2359页

作者： Huang, Bruce Wang, I-Hsiang University of California Department of Electrical and Computer Engineering Los Angeles United States National Taiwan University Graduate Institute of Communication Engineering Department of Electrical Engineering Taipei Taiwan

Fundamental limits on the error probabilities of a family of decentralized detection algorithms (eg., the social learning rule proposed by Lalitha et al. [2]) over directed graphs are investigated. In decentralized detection, a network of nodes locally exchanging information about the samples they observe with their neighbors to collectively infer the underlying unknown hypothesis. Each node in the network weighs the messages received from its neighbors to form its private belief and only requires knowledge of the data generating distribution of its observation. In this work, it is first shown that while the original social learning rule of Lalitha et al. [2] achieves asymptotically vanishing error probabilities as the number of samples tends to infinity, it suffers a gap in the achievable error exponent compared to the centralized case. The gap is due to the network imbalance caused by the local weights that each node chooses to weigh the messages received from its neighbors. To close this gap, a modified learning rule is proposed and shown to achieve error exponents as large as those in the centralized setup. This implies that there is essentially no first-order penalty caused by decentralization in the exponentially decaying rate of error probabilities. To elucidate the price of decentralization, further analysis on the higher-order asymptotics of the error probability is conducted. It turns out that the price is at most a constant multiplicative factor in the error probability, equivalent to an o(1/t) additive gap in the error exponent, where t is the number of samples observed by each agent in the network and the number of rounds of information exchange. This constant depends on the network connectivity and captures the level of network imbalance. Results of simulation on the error probability supporting our learning rule are shown. Further discussions and extensions of results are also presented. © 1963-2012 IEEE.

关键词： Convergence Testing Vectors Error probability Network topology Directed graphs Training Symmetric matrices Iterative methods Internet of Things

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Developed a Machine Learning and Deep Learning Model for 5G MIMO Data Based Beam Selection and Intelligent Network Analytics

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SN computer Science 2025年第3期6卷 1-21页

作者： Ashok, P. Suganya, R. Lekha, P. Pandian, Poornima Subashini, M. Nithya, M. Department of Computer Science and Engineering Sri Sai Ram Institute of Technology Tamilnadu India Department of Computer and Communication Engineering Sri Sai Ram Institute of Technology Tamilnadu India Department of Electronics and Communication Engineering Sri Sairam Engineering College Tamilnadu India Department of Computer Science and Engineering Sri Sairam Engineering College Tamilnadu India

Introduces an innovative application of 5G MIMO data in singular value decomposition (SVD) focusing on deep learning beam-selection and intelligent network analytics. This research explores the potential of leveraging 5G MIMO data to enhance network performance and efficiency through advanced ML techniques. Deep learning algorithms are employed for beam-selection, optimizing the transmission of data in 5G networks to improve throughput and reliability. Intelligent network analytics are applied to extract valuable insights from the vast amount of data generated by 5G MIMO systems, enabling proactive network management and optimization. By harnessing the capabilities of deep learning and intelligent analytics, this research aims to address key challenges in 5G network optimization and management. The integration of SVD techniques with 5G MIMO data offers a novel approach to enhancing network performance, enabling more efficient utilization of resources and improved quality of service for users. The insights gained from intelligent network analytics facilitate informed decision-making and proactive maintenance leading to greater reliability and resilience in 5G networks. This research contributes to the advancement of 5G technology by leveraging ML to unlock the full potential of MIMO data for optimizing network performance and enabling intelligent network management. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： 5G Beam-selection Deep learning Intelligent management MIMO Network analytics Network performance Optimization Proactive maintenance Singular value decomposition

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