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学者姓名：郑相涵
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In recent years, facial recognition technology has been widely adopted in modern society. However, the plaintext storage, computation, and transmission of facial data have posed significant risks of information leakage. To address this issue, this paper proposes a facial recognition framework based on approximate homomorphic encryption (HE_FaceNet), aimed at effectively mitigating privacy leaks during the facial recognition process. The framework first utilizes a pre-trained model to extract facial feature templates, which are then encrypted. The encrypted templates are matched using Euclidean distance, with the final recognition being performed after decryption. However, the time-consuming nature of homomorphic encryption calculations limits the practical applicability of the HE_FaceNet framework. To overcome this limitation, this paper introduces an optimization scheme based on clustering algorithms to accelerate the facial recognition process within the HE_FaceNet framework. By grouping similar faces into clusters through clustering analysis, the efficiency of searching encrypted feature values is significantly improved. Performance analysis indicates that the HE_FaceNet framework successfully protects facial data privacy while maintaining high recognition accuracy, and the optimization scheme demonstrates high accuracy and significant computational efficiency across facial datasets of varying sizes.
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| GB/T 7714 | Song, Zhigang , Wang, Gong , Yang, Wenqin et al. Privacy-preserving method for face recognition based on homomorphic encryption [J]. | PLOS ONE , 2025 , 20 (2) . |
| MLA | Song, Zhigang et al. "Privacy-preserving method for face recognition based on homomorphic encryption" . | PLOS ONE 20 . 2 (2025) . |
| APA | Song, Zhigang , Wang, Gong , Yang, Wenqin , Li, Yunliang , Yu, Yinsheng , Wang, Zeli et al. Privacy-preserving method for face recognition based on homomorphic encryption . | PLOS ONE , 2025 , 20 (2) . |
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With the wide application of blockchain comes the challenge of cross-chain interaction. For example, the isolation between the information stored in different blockchains can result in the "isolated islands of value" effect in blockchains. In addition, conventional cross-chain methods have a number of drawbacks such as centralization, or have a very restricted application scope. (e.g., hash-locking can only be utilized for asset exchanges, but asset transfer is a more common and demanding requirement). Other solutions like sidechain may be an overkill (e.g., too complex) for simple applications. To address these challenges, we propose an cross-chain transaction scheme that integrates hash-locking with notary mechanisms to enable decentralized asset transfers. By embedding notary scheme within the hash-lock structure, our approach ensures support for asset transfers while mitigating centralization risks. Additionally, we enhance the traditional hash-locking mechanism, making it more flexible and suitable for diverse cross-chain scenarios. This combination of hash-locking and notary scheme allows our scheme to overcome the original shortcomings of them both, while maintaining high efficiency and security. In addition to designing the transaction method, we also constructed a transaction model, conducted experiments on transaction gas consumption, performed a detailed security analysis, and presented an in-depth discussion of our scheme while proposing directions for future work. Experiment and analysis demonstrate the practicability of our scheme in terms of performance and security.
Keyword ：
Blockchain Blockchain Cross-chain Cross-chain Decentralization Decentralization Hash-locking Hash-locking Notary scheme Notary scheme Smart contract Smart contract
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| GB/T 7714 | Zou, Huiying , Sun, Hua , Chen, Sheng et al. DeCoTa: a lightweight decentralized cross-chain transfer scheme based on hash-locking [J]. | SCIENTIFIC REPORTS , 2025 , 15 (1) . |
| MLA | Zou, Huiying et al. "DeCoTa: a lightweight decentralized cross-chain transfer scheme based on hash-locking" . | SCIENTIFIC REPORTS 15 . 1 (2025) . |
| APA | Zou, Huiying , Sun, Hua , Chen, Sheng , Ren, Wei , Zheng, Xianghan . DeCoTa: a lightweight decentralized cross-chain transfer scheme based on hash-locking . | SCIENTIFIC REPORTS , 2025 , 15 (1) . |
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The Parkinson’s disease gait classification method proposed in this paper consists of five steps. Firstly, gait data collection includes gait videos of both normal individuals and Parkinson’s patients provided by the Gait Laboratory of Fujian University of Traditional Chinese Medicine. After gait data collection, preprocessing of the data is conducted, including data augmentation, cropping, resizing, and adding dynamic blur. Next, the Attention-LSTM model is constructed to effectively capture long-term dependencies in time series. After training and testing, the model can achieve effective classification of normal individuals and Parkinson’s patients based on gait video data, yielding results. The experiments demonstrate that the model constructed in this paper outperforms baseline models and previous Parkinson’s disease classification studies based on video data. Our research reduces the difficulty and cost of gait data collection, enabling contactless gait analysis. This will further reduce the complexity and cost of Parkinson’s disease diagnosis and lay a solid foundation for remote diagnosis of Parkinson’s disease. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.
Keyword ：
Diagnosis Diagnosis Neurodegenerative diseases Neurodegenerative diseases
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| GB/T 7714 | Zhang, Jianxian , Zheng, Xianghan , Wang, Huan et al. A Parkinson’s Disease Gait Identification Model Based on Attention Mechanism and Long Short-Term Memory Network [C] . 2025 : 251-263 . |
| MLA | Zhang, Jianxian et al. "A Parkinson’s Disease Gait Identification Model Based on Attention Mechanism and Long Short-Term Memory Network" . (2025) : 251-263 . |
| APA | Zhang, Jianxian , Zheng, Xianghan , Wang, Huan , Cai, Jing , Liu, Ying . A Parkinson’s Disease Gait Identification Model Based on Attention Mechanism and Long Short-Term Memory Network . (2025) : 251-263 . |
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As a data-driven science, machine learning requires vast amounts of training data and computational resources. However, for highly privacy-sensitive data, it is crucial to protect the privacy of the data during both the training and utilization of machine learning models. In this paper, we propose a privacy-preserving machine learning approach using autoencoders and differential privacy mechanisms to safeguard data privacy while minimizing the impact on data availability. Specifically, we augment logistic regression and ResNet18 models with different architectures of autoencoders to perform data encryption? without compromising the machine learning tasks. Additionally, we employ differential privacy mechanisms to introduce gradient perturbations in the encoding part of the autoencoder, enhancing the algorithm’s security and further protecting data privacy. We also design the cosine similarity between the encoded and original data as a metric for evaluating data privacy, considering model performance, privacy budget, and data privacy collectively to balance data availability and privacy. Extensive experiments conducted on MNIST, CIFAR-10, PathMNIST, and BloodMNIST datasets demonstrate that for simple logistic regression models handling easily classifiable datasets, employing simple autoencoder structures can enhance classification accuracy, with significant performance impact after adding differential privacy. For ResNet18, utilizing convolutional autoencoders for data encryption generally has minimal impact on model classification performance and can even improve accuracy in most cases. Adding differential privacy has minor effects on model classification performance. Selecting appropriate model structures and privacy budgets for different usage scenarios can ensure both data availability and privacy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Keyword ：
Adversarial machine learning Adversarial machine learning Differential privacy Differential privacy
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| GB/T 7714 | Wu, Jiayun , Ren, Wei , Zhang, Xianchao et al. An Encoder-Based Framework for Privacy-Preserving Machine Learning [C] . 2025 : 37-46 . |
| MLA | Wu, Jiayun et al. "An Encoder-Based Framework for Privacy-Preserving Machine Learning" . (2025) : 37-46 . |
| APA | Wu, Jiayun , Ren, Wei , Zhang, Xianchao , Zheng, Xianghan . An Encoder-Based Framework for Privacy-Preserving Machine Learning . (2025) : 37-46 . |
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Data watermark is of the surmount importance for data trading. Most of the data in the current data trading market is stored in the form of spreadsheets, yet there are few watermarking schemes specifically designed for tables. We propose a bitmap-based watermark scheme for table data. Our approach involves converting the table into a bitmap representation and utilizing classical image processing algorithms to embed a watermark that incorporates error correction codes. The watermarked data is then returned to its corresponding positions in the table. During the table conversion process, we apply the principle of minimal modification to minimize the impact of watermark embedding on the table data. Experimental evaluations demonstrate the effectiveness of the proposed scheme in preserving data integrity and accurately extracting watermarks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Keyword ：
Data assimilation Data assimilation Data integrity Data integrity
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| GB/T 7714 | Liu, Xinyu , Ren, Wei , Liu, Wenmao et al. A Data Watermark Scheme Base on Data Converted Bitmap for Data Trading [C] . 2025 : 270-289 . |
| MLA | Liu, Xinyu et al. "A Data Watermark Scheme Base on Data Converted Bitmap for Data Trading" . (2025) : 270-289 . |
| APA | Liu, Xinyu , Ren, Wei , Liu, Wenmao , Zheng, Xianghan . A Data Watermark Scheme Base on Data Converted Bitmap for Data Trading . (2025) : 270-289 . |
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Semi-supervised learning (SSL) employs unlabeled data with limited labeled samples to enhance deep networks, but imbalance degrades performance due to biased pseudo-labels skewing decision boundaries. To address this challenge, we propose two optimization conditions inspired by our theoretical analysis. These conditions focus on aligning class distributions and representations. Additionally, we introduce a plug-and-play method called Basis Transformation based distribution alignment (BTDA) that efficiently aligns class distributions while considering inter-class relationships. BTDA mitigates the negative impact of biased pseudo-labels through basis transformation, which involves a learnable transition matrix. Extensive experiments demonstrate the effectiveness of integrating existing SSL methods with BTDA in image classification tasks with class imbalance. For example, BTDA achieves accuracy improvements ranging from 2.47 to 6.66% on CIFAR10-LT and SVHN-LT datasets, and a remarkable 10.95% improvement on the tail class, even under high imbalanced rates. Despite its simplicity, BTDA achieves state-of-the-art performance in SSL with class imbalance on representative datasets.
Keyword ：
Basis transformation Basis transformation Class-imbalanced datasets Class-imbalanced datasets Distribution alignment Distribution alignment Image classification Image classification Inter-class bias Inter-class bias Semi-supervised learning Semi-supervised learning
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| GB/T 7714 | Ye, Jinhuang , Gao, Xiaozhi , Li, Zuoyong et al. Btda: basis transformation based distribution alignment for imbalanced semi-supervised learning [J]. | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS , 2024 , 15 (9) : 3829-3845 . |
| MLA | Ye, Jinhuang et al. "Btda: basis transformation based distribution alignment for imbalanced semi-supervised learning" . | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 15 . 9 (2024) : 3829-3845 . |
| APA | Ye, Jinhuang , Gao, Xiaozhi , Li, Zuoyong , Wu, Jiawei , Xu, Xiaofeng , Zheng, Xianghan . Btda: basis transformation based distribution alignment for imbalanced semi-supervised learning . | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS , 2024 , 15 (9) , 3829-3845 . |
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Secure group communication in Vehicle Ad hoc Networks (VANETs) over open channels remains a challenging task. To enable secure group communications with conditional privacy, it is necessary to establish a secure session using Authenticated Key Agreement (AKA). However, existing AKAs suffer from problems such as cross-domain dynamic group session key negotiation and heavy computational burdens on the Trusted Authority (TA) and vehicles. To address these challenges, we propose a dynamic privacy-preserving anonymous authentication scheme for condition matching in fog-cloud-based VANETs. The scheme employs general Elliptic Curve Cryptosystem (ECC) technology and fog-cloud computing methods to decrease computational overhead for On-Board Units (OBUs) and supports multiple TAs for improved service quality and robustness. Furthermore, certificateless technology alleviates TAs of key management burdens. The security analysis indicates that our solution satisfies the communication security and privacy requirements. Experimental simulations verify that our method achieves optimal overall performance with lower computational costs and smaller communication overhead compared to state-of-the-art solutions.
Keyword ：
authenticated key agreement authenticated key agreement conditional privacy-preserving conditional privacy-preserving dynamic group dynamic group fog-cloud computing fog-cloud computing VANETs VANETs
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| GB/T 7714 | Zhan, Yonghua , Xie, Weipeng , Shi, Rui et al. Dynamic Privacy-Preserving Anonymous Authentication Scheme for Condition-Matching in Fog-Cloud-Based VANETs [J]. | SENSORS , 2024 , 24 (6) . |
| MLA | Zhan, Yonghua et al. "Dynamic Privacy-Preserving Anonymous Authentication Scheme for Condition-Matching in Fog-Cloud-Based VANETs" . | SENSORS 24 . 6 (2024) . |
| APA | Zhan, Yonghua , Xie, Weipeng , Shi, Rui , Huang, Yunhu , Zheng, Xianghan . Dynamic Privacy-Preserving Anonymous Authentication Scheme for Condition-Matching in Fog-Cloud-Based VANETs . | SENSORS , 2024 , 24 (6) . |
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With the rapid development of Ethereum, vast amounts of data are recorded on the blockchain through transactions, encompassing diverse and extensive textual information. While Long Short-Term Memory (LSTM) models have shown remarkable effectiveness in sentiment analysis tasks in recent years, they often encounter situations where different features have equal importance when processing such textual data. Therefore, this study introduces a Bidirectional LSTM model with a Multi-Head Attention mechanism (MABLSTM) designed for sentiment analysis tasks in Ethereum transaction texts. BLSTM consists of two distinct and independent LSTMs that consider information flow from two directions, capturing contextual information from both the past and the future. The outputs from the BLSTM layer are enhanced using a multi-head attention mechanism to amplify the importance of sentiment words and blockchain-specific terms. This paper evaluates the effectiveness of MABLSTM on Ethereum transaction data through experiments conducted on an Ethereum transaction dataset, comparing MABLSTM with CNN, SVM, ABLSTM and ABCDM. The results demonstrate the effectiveness and superiority of MABLSTM in sentiment analysis tasks. This approach accurately analyzes sentiment polarity in Ethereum transaction texts, providing valuable information for Ethereum participants and researchers to support decision-making and emotional analysis.
Keyword ：
Attention Mechanism Attention Mechanism BLSTM BLSTM Deep Learning Deep Learning Ethereum Ethereum MABLST MABLST Sentiment Analysis Sentiment Analysis
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| GB/T 7714 | Zheng, Xianghan , Zhang, Wenyan , Zhang, Jianxian et al. Ethereum Public Opinion Analysis Based on Attention Mechanism [J]. | COGNITIVE COMPUTING - ICCC 2023 , 2024 , 14207 : 100-115 . |
| MLA | Zheng, Xianghan et al. "Ethereum Public Opinion Analysis Based on Attention Mechanism" . | COGNITIVE COMPUTING - ICCC 2023 14207 (2024) : 100-115 . |
| APA | Zheng, Xianghan , Zhang, Wenyan , Zhang, Jianxian , Xie, Weipeng . Ethereum Public Opinion Analysis Based on Attention Mechanism . | COGNITIVE COMPUTING - ICCC 2023 , 2024 , 14207 , 100-115 . |
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In the context of construction and demolition waste exacerbating environmental pollution, the lack of recycling technology has hindered the green development of the industry. Previous studies have explored robot-based automated recycling methods, but their efficiency is limited by movement speed and detection range, so there is an urgent need to integrate drones into the recycling field to improve construction waste management efficiency. Preliminary investigations have shown that previous construction waste recognition techniques are ineffective when applied to UAVs and also lack a method to accurately convert waste locations in images to actual coordinates. Therefore, this study proposes a new method for autonomously labeling the location of construction waste using UAVs. Using images captured by UAVs, we compiled an image dataset and proposed a high-precision, long-range construction waste recognition algorithm. In addition, we proposed a method to convert the pixel positions of targets to actual positions. Finally, the study verified the effectiveness of the proposed method through experiments. Experimental results demonstrated that the approach proposed in this study enhanced the discernibility of computer vision algorithms towards small targets and high-frequency details within images. In a construction waste localization task using drones, involving high-resolution image recognition, the accuracy and recall were significantly improved by about 2% at speeds of up to 28 fps. The results of this study can guarantee the efficient application of drones to construction sites.
Keyword ：
computer vision computer vision construction waste management construction waste management long-distance target detection long-distance target detection unmanned aerial vehicle unmanned aerial vehicle
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| GB/T 7714 | Wang, Zeli , Yang, Xincong , Zheng, Xianghan et al. Vision-Based On-Site Construction Waste Localization Using Unmanned Aerial Vehicle [J]. | SENSORS , 2024 , 24 (9) . |
| MLA | Wang, Zeli et al. "Vision-Based On-Site Construction Waste Localization Using Unmanned Aerial Vehicle" . | SENSORS 24 . 9 (2024) . |
| APA | Wang, Zeli , Yang, Xincong , Zheng, Xianghan , Li, Heng . Vision-Based On-Site Construction Waste Localization Using Unmanned Aerial Vehicle . | SENSORS , 2024 , 24 (9) . |
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Numerous users from diverse domains access information and perform various operations in multi-domain environments. These users have complex permissions that increase the risk of identity falsification, unauthorized access, and privacy breaches during cross-domain interactions. Consequently, implementing an access control architecture to prevent users from engaging in illicit activities is imperative. This paper proposes a novel blockchain-based access control architecture for multi-domain environments. By integrating the multi-domain environment within a federated chain, the architecture utilizes Decentralized Identifiers (DIDs) for user identification and relies on public/secret key pairs for operational execution. Verifiable credentials are used to authorize permissions and release resources, thereby ensuring authentication and preventing tampering and forgery. In addition, the architecture automates the authorization and access control processes through smart contracts, thereby eliminating human intervention. Finally, we performed a simulation evaluation of the architecture. The most time-consuming process had a runtime of 1074 ms, primarily attributed to interactions with the blockchain. Concurrent testing revealed that with a concurrency level of 2000 demonstrated that the response times for read and write operations were maintained within 1000 ms and 4600 ms, respectively. In terms of storage efficiency, except for user registration, which incurred two gas charges, all the other processes required only one charge.
Keyword ：
Access control Access control Blockchain Blockchain DIDs DIDs Multi-domain environments Multi-domain environments Smart contracts Smart contracts Verifiable credentials Verifiable credentials
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| GB/T 7714 | Du, Zhiqiang , Li, Yunliang , Fu, Yanfang et al. Blockchain-based access control architecture for multi-domain environments [J]. | PERVASIVE AND MOBILE COMPUTING , 2024 , 98 . |
| MLA | Du, Zhiqiang et al. "Blockchain-based access control architecture for multi-domain environments" . | PERVASIVE AND MOBILE COMPUTING 98 (2024) . |
| APA | Du, Zhiqiang , Li, Yunliang , Fu, Yanfang , Zheng, Xianghan . Blockchain-based access control architecture for multi-domain environments . | PERVASIVE AND MOBILE COMPUTING , 2024 , 98 . |
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