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Privacy-Enhanced Role-Based Access Control for IoT Systems

IEEE Internet of Things Journal 2025年

作者： Zhang, Yawei Chen, Lanxiang Zhu, Yizhao Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fuzhou350117 China City University of Macau Faculty of Data Science China

Unauthorized accessing private information poses a significant security risk in IoT. Although role-based access control can partially address the problem, there is a lack of studies on protecting the privacy of roles. This work proposes a novel privacy-enhanced role-based access control (PE-RBAC) scheme for resource-limited cloud-based IoT systems. Our PE-RBAC scheme enhances the privacy of traditional RBAC by allowing the cloud platform (CP) to learn only the necessary roles of IoT devices rather than their entire role sets. IoT devices remain unaware of the cloud platform’s roles through the incorporation of the authorized private set intersection (APSI) protocol. Our scheme enables role authorization to prevent unauthorized requests by integrating an authorization center (AC) with an authentication scheme. It achieves lightweight computation and communication overhead using a garbled Bloom filter (GBF), independent of IoT roles. It requires only XOR operations and random number generation, making it ideal for IoT devices with limited computing capacity and bandwidth. Through formal security model definitions and rigorous security analysis, it achieves unlinkability and ciphertext indistinguishability for IoT device privacy and unforgeability for both the cloud platform and IoT devices. It demonstrates high practicality through real-world-like experiments considering varying role sizes and the number of concurrent IoT devices. Experimental results show a throughput of 1,100 requests per second, with storage overhead remaining linear with the number of devices. © 2014 IEEE.

关键词： Cloud platforms

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Get the Punchline: Headline-Centric Multi-View Learning for Personalized Headline Generation

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 962-973页

作者： Wenya Guo Shenglong Yu Ying Zhang Zhengkun Zhang Xiaojie Yuan College of Computer Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology College of Cyber Science Nankai University Tianjin China

Personalized news headline generation aims to generate a user-specific headline based on their reading histories. Existing approaches incorporate information from various aspects of news articles, which leads to a lack of focus on news headlines and fails to capture accurate information for guiding headline generation. As a result, the generated headlines lack differences across users and fail to reflect user-specific information. To address this problem, we propose a Headline-centric Multi- view Headline Generation network, denoted as HMvNet. HMvNet is an extraction-integration method that centers around the punchline, i.e. the user-specific information contained in history headlines. HMvNet extracts user-preferred entity tokens and syntactic structures from history headlines through the token-level view and the sequence-level view, respectively. Besides, we propose a reinforcement learning method specifically designed for personalized headline generation to train HMvNet. This method allows the model to fully leverage extracted information and ensures the fluency of the generated headlines. Experimental results demonstrate the superiority of the proposed personalization modeling method as well as the reinforcement learning framework.

关键词： History Data mining Syntactics Reinforcement learning Recommender systems Generators Training User preference Speech processing Hybrid power systems

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Across-Platform Detection of Malicious Cryptocurrency Accounts via Interaction Feature Learning

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IEEE Transactions on information Forensics and security 2025年 20卷 4783-4798页

作者： Che, Zheng Shen, Meng Tan, Zhehui Du, Hanbiao Wang, Wei Chen, Ting Zhao, Qinglin Xie, Yong Zhu, Liehuang Beijing Institute of Technology School of Computer Science Beijing100081 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Xi'an Jiaotong University Key Laboratory for Intelligent Networks and Network Security Ministry of Education Xi'an710049 China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Macau University of Science and Technology School of Computer Science and Engineering Taipa China Qinghai Institute of Technology School of Computer and Information Science Xining810016 China

With the rapid evolution of Web3.0, cryptocurrency has become a cornerstone of decentralized finance. While these digital assets enable efficient and borderless financial transactions, their pseudonymous nature has also attracted malicious activities such as money laundering, fraud, and other financial crimes. Effective detection of malicious accounts is crucial to maintaining the security and integrity of the Web 3.0 ecosystem. Existing malicious account detection methods rely on large amounts of labeled data and suffer from low generalization. Label-efficient and generalizable malicious account detection remains a challenging task. In this paper, we propose ShadowEyes, a framework for detecting malicious accounts by leveraging interaction feature learning with only a small labeled dataset. Specifically, We first propose a generalized account representation named TxGraph, which captures the universal interaction features of Ethereum and Bitcoin. Then we carefully design an account representation augmentation method tailored to simulate the evolution of malicious accounts to generate positive pairs. We conduct extensive experiments using public datasets to evaluate the performance of ShadowEyes. The results demonstrate that it outperforms state-of-the-art (SOTA) methods in four typical scenarios. Specifically, in the scenario of across-platform malicious account detection, ShadowEyes maintains an F1 score of around 90%, which is 10% higher than the SOTA method. In the zero-shot learning scenario, it can achieve an F1 score of 79.56% for detecting gambling accounts, surpassing the SOTA method by 10.44%. © 2005-2012 IEEE.

关键词： Zero-shot learning

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Voiceprint recognition model based on Ecapa-Tdnn and ResNet 5

Voiceprint recognition model based on Ecapa-Tdnn and ResNet

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5th International Conference on Telecommunications, Optics, and computer Science, TOCS 2024

作者： Jia, Chengjie Fei, Rong Li, Xiaomeng School of Computer Science and Engineering Xi'an University of Technology Shaanxi Xi'an 710004 China Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi'an University of Technology Shaanxi Xi'an 710048 China

ISBN: (纸本)9781510691667

In recent years, with the continuous progress of deep learning neural networks and the construction of large data sets, new breakthroughs in voiceprint verification technology for voice calls have been rapidly achieved. Deep learning-based neural networks, such as X-Vectors, ResNets, Ecapa-Tdnn, etc., are able to utilize their own network characteristics to extract rich voiceprint features, thus further improving the accuracy of voiceprint recognition, but there is still room for improvement in the performance and parameter count of the traditional models. In this paper, we propose the Ecapa2Tdnn model based on EcapaTdnn and ResNet, which has the advantage of possessing a deeper network architecture while performing better in terms of temporal modeling capability. This indicates that the model can effectively handle long time series of speech data, thus improving the performance of the voiceprint recognition system. The experimental results show that under the same experimental environment, compared with the equal error rate (EER) of 11.31% of the EcapaTdnn model, the Ecapa2Tdnn model proposed in this paper has only an EER of 10.27%, which is 1.04% lower than the former EER, and the number of parameters is still one-fourth of the original model. The model improves accuracy while reducing the total number of parameters, which is expected to be widely used in the field of voiceprint recognition. © 2025 SPIE.

关键词： Deep neural network EcapaTdnn ResNet voiceprint recognition model

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Research and Implementation of Trusted Blockchain Core Technology Based on State Secret Algorithm

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China Communications 2025年第4期22卷 143-160页

作者： Zheng Qingan Meng Jialin Wu Junjie Li Jingtao Lin Haonan Computer Science and Information Security Management Department Fujian Police CollegeFuzhou 350007China Fujian Province Key Laboratory for Digital Network Crime Supervision and Prevention Fuzhou 350007China ByteDance Technology(Beijing)Co. Ltd.Haidian DistrictBeijing 100080China Dongguan Water Group Co. LtdDongguan 523000China Fuqing Municipal Public Security Bureau Fuzhou 350301China

With the rapid development of blockchain technology,the Chinese government has proposed that the commercial use of blockchain services in China should support the national encryption standard,also known as the state secret algorithm GuoMi *** original Hyperledger Fabric only supports internationally common encryption algorithms,so it is particularly necessary to enhance support for the national encryption *** identity authentication,access control,and security audit technologies have single-point failures,and data can be easily tampered with,leading to trust *** address these problems,this paper proposes an optimized and application research plan for Hyperledger *** study the optimization model of cryptographic components in Hyperledger Fabric,and based on Fabric's pluggable mechanism,we enhance the Fabric architecture with the national encryption *** addition,we research key technologies involved in the secure application protocol based on the *** propose a blockchain-based identity authentication protocol,detailing the design of an identity authentication scheme based on blockchain certificates and Fabric CA,and use a dual-signature method to further improve its security and ***,we propose a flexible,dynamically configurable real-time access control and security audit mechanism based on blockchain,further enhancing the security of the system.

关键词： access control authentication Hyperledger Fabric security audit state secret algorithm

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Recipient-Aware Photo Automatic Deletion Control Policy Recommendation Scheme in Online Social networks

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2352-2366页

作者： Luo, Haiyang Sun, Zhe Sun, Yunqing Li, Ang Wang, Binghui Cao, Jin Niu, Ben Chinese Academy of Sciences Institute of Information Engineering Beijing100085 China University of Chinese Academy of Sciences School of Cyber Security Beijing100049 China Key Laboratory of Cyberspace Security Defense Beijing100085 China Guangdong Guangzhou510006 China Northwestern University McCormick School of Engineering and Applied Science EvanstonIL60208 United States University of Maryland Department of Electrical and Computer Engineering College ParkMD20742 United States Illinois Institute of Technology Department of Computer Science ChicagoIL60616 United States Xidian University State Key Laboratory of Integrated Services Networks School of Cyber Engineering Shaanxi Xi’an710071 China

Content sharing, whether in Online Social networks (OSNs) or even in the Internet of Things (IoT), serves as a pivotal link in the flow of data. To better protect the privacy of shared content, current OSNs allow sharers to manually set policies for uploaded content. However, this method of policy setting is not suitable for scenarios where IoT is deeply integrated with OSNs, as IoT devices often share content frequently and automatically. To address this issue, we propose the design, implementation, and evaluation of SmartCircles, a personalized photo-sharing and automatic deletion scheme. SmartCircles can function as a plugin within existing OSNs, supporting operations on various smart devices. It encompasses the following steps: a) Before sharing a photo, calculate the intimacy level depicted in the photo and the sharer's willingness to share. b) Before the recipient views the photo, calculate the intimacy between the sharer and the recipient, and evaluate feedback from the recipient. c) Based on the results computed above and a trade-off between profit and loss, recommend a recipient-aware automatic deletion control policy for the photo. We implement a prototype of SmartCircles, and the evaluation results demonstrate its effectiveness with an accuracy rate of policy recommendations reaching approximately 92%. © 2004-2012 IEEE.

关键词： Differential privacy

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ALFC-Point: Adaptive Laplacian Feature Convolution network for 3D Point Cloud Understanding 41st

ALFC-Point: Adaptive Laplacian Feature Convolution Network f...

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41st computer Graphics International Conference, CGI 2024

作者： Liao, Junjie Yin, Mengxiao Li, Ming Zhu, Congyang Yang, Zhiqiang Zhan, Feng School of Computer and Electronics Information Guangxi University Guangxi Nanning530004 China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi Nanning530004 China Education Department of Guangxi Zhuang Autonomous Region Nanning China

ISBN: (纸本)9783031820236

To capture 3D geometric details, point cloud understanding primarily employs convolution, dynamic map, or transformer methods to build complex local geometry extractors. When dealing with sparse and noisy point clouds in real-world environments, the structural design of these methods tends to be complex and perform poorly. In this paper, we propose a lightweight point cloud understanding framework based on Laplacian feature convolution, which has high accuracy on normal-sized point cloud datasets and performs well when dealing with sparse and noisy point cloud inputs. The adaptive Laplacian convolution module combined with the vector attention mechanism can effectively capture the local details of point cloud geometry. The framework employs a multi-scale feature fusion mechanism between modules to reduce the number of sampling points step by step, reducing redundant computations and achieving a lightweight effect. In addition, we propose a parameter-free module for interpolation to enhance the training part. Experimental results on object-level datasets show that the lightweight method proposed in our paper has similar accuracy to the current best method in terms of accuracy (Acc) and mean intersection over union (mIoU), and has better robustness in handling sparse and noisy point clouds. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Laplace transforms

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Spatial-Temporal Semantic Feature Interaction network for Semantic Change Detection in Remote Sensing Images

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12090-12102页

作者： Zhang, Yuhang Zhang, Wuxia Ding, Songtao Wu, Siyuan Lu, Xiaoqiang Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing School of Computer Science and Technology Xi'an710121 China Xi'an University of Technology College of Computer Science and Engineering Shaanxi Xi'an710048 China Fuzhou University College of Physics and Information Engineering Fuzhou350002 China

Semantic Change Detection (SCD) in Remote Sensing Images (RSI) aims to identify changes in the type of Land Cover/Land Use (LCLU) corresponding to changed areas in RSI. The "from-to" information of the acquired image has more profound practical significance than Binary Change Detection (BCD). However, most deep learning-based SCD algorithms do not fully exploit the spatial-temporal information of multi-level features, leading to challenges in extracting LCLU features in complex changing scenes. To address these issues, we propose a Spatial-Temporal Semantic Feature Interaction network (STS-FINet) to improve the performance of SCD in RSI. The proposed STS-FINet comprises a Multi-Scale Feature Extraction Encoder (MS-FEE), a Transformer-based Multi-Level Feature Interaction module (TML-FI), and a Multi-Level Feature Fusion Decoder (ML-FFD). The MS-FEE extracts deep semantic and differential information from the RSI. The TML-FI is designed to mine the spatial-temporal information by extracting long-range dependencies and spatial information from multi-level features to improve spatial perception. Moreover, Mixed Spatial Reasoning Convolution block (MixSrc) is presented to enrich the spatial information by extracting the multi-scale features, thus improving the model's capability to interpret complex changing scenes. Finally, ML-FFD integrates the multi-level features, resulting in the generation of the semantic change map. The effectiveness of the proposed STS-FINet is verified on two high-resolution RSI datasets. Experimental results show that the proposed STS-FINet achieves better change detection performance than SOTA methods. © 2008-2012 IEEE.

关键词： Change detection

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Pairing-Free Blockchain-Assisted Certificateless Aggregation Signcryption Scheme for VANETs

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IEEE Internet of Things Journal 2025年第11期12卷 15545-15557页

作者： Wang, Yahui Peng, Cong Jia, Xiaoying Wen, Jiaming Zhang, Yuanyuan Xinyang Normal University School of Computer and Information Technology School of Software Xinyang464000 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China South-Central Minzu University School of Mathematics and Statistics Wuhan430074 China Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China University of Wollongong Institute of Cybersecurity and Cryptology School of Computing and Information Technology WollongongNSW2522 Australia Hubei University of Technology School of computer science Wuhan430072 China

Smart vehicle applications play a crucial role in intelligent transportation systems, enabling sensor-equipped vehicles to establish dynamic networks for efficient collection, sharing, and aggregation. This significantly enhances road security and efficiency by transmitting crucial information to traffic authorities. However, partial research on certificateless aggregation signcryption (CLAS) scheme reveals an intriguing phenomenon where each proposed scheme consistently exhibits numerous security vulnerabilities, particularly susceptible to public key replacement attacks. To address these challenges, this paper proposes a blockchain-assisted certificateless aggregation sign-cryption scheme (BACLAS), leveraging blockchain technology to securely store users' public key on a distributed ledger and prevent public key replacement attacks effectively. Furthermore, it is a provably secure communication scheme for real-world Vehicle-to-Infrastructure (V2I) communication while addressing practical security concerns. The BACLAS scheme ensures security in terms of existential unforgeability against adaptive chosen message attacks (EUF-CMA) and indistinguishability against adaptive chosen ciphertext attacks (IND-CCA2) based on the hardness assumption of the elliptic curve discrete logarithm problem and computational Diffie-Hellman problem in the random oracle model. The proposed scheme effectively reduces computational costs and time consumption, resulting in a significant reduction of the computation burden ranging from 50.02% to 88.28% compared to other competitive schemes. Moreover, it successfully addresses the key-escrow problem, thereby achieving enhanced security properties. © 2014 IEEE.

关键词： Vehicular ad hoc networks

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network intrusion detection based on feature fusion of attack dimension

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Journal of Supercomputing 2025年第6期81卷 1-33页

作者： Sun, Xiaolong Gu, Zhengyao Zhang, Hao Gu, Jason Liu, Yanhua Dong, Chen Ye, Junwei College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Chinese Ministry of Education Fuzhou350116 China Department of Electrical and Computer Engineering Dalhousie University HalifaxNSB3J 1Z1 Canada

network traffic anomaly detection involves the rapid identification of intrusions within a network through the detection, analysis, and classification of network traffic data. The variety of cyberattacks encompasses diverse attack principles. Employing an indiscriminate feature selection strategy may lead to the neglect of key features highly correlated with specific attack types. This oversight could diminish the recognition rate for that category, thereby impacting the overall performance of the detection model. To address this issue, this paper proposes a network traffic anomaly detection model based on the fusion of attack-dimensional features. Firstly, construct binary classification datasets independently for each attack class and perform individual feature selection to extract positively correlated features for each class. The features are then fused by employing a combination methods. Subsequently, based on the fused sub-datasets, base classifiers are trained. Finally, an ensemble learning approach is introduced to integrate the predictions of individual classifiers, enhancing the robustness of the model. The proposed approach, validated on NSL-KDD and UNSW-NB15 benchmark datasets, outperforms the latest methods in the field by achieving a 2% and 7% increase in precision on weighted averages. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Feature Selection

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