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Method and Practice of Trusted Embedded computing and Data Transmission Protection Architecture Based on Android

Chinese Journal of Electronics 2024年第3期33卷 623-634页

作者： Yichuan WANG Wen GAO Xinhong HEI Yanning DU School of Computer Science and Engineering Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology

In recent years,the rapid development of Internet technology has constantly enriched people's daily life and gradually changed from the traditional computer terminal to the mobile *** with it comes the security problems brought by the mobile *** for Android system,due to its open source nature,malicious applications continue to emerge,which greatly threatens the data security of ***,this paper proposes a method of trusted embedded static measurement and data transmission protection architecture based on Android to reduce the risk of data leakage in the process of terminal storage and *** conducted detailed data and feasibility analysis of the proposed method from the aspects of time consumption,storage overhead and *** experimental results show that this method can detect Android system layer attacks such as self-booting of the malicious module and improve the security of data encryption and transmission process *** with the native system,the additional performance overhead is small.

关键词： TrustZone Static measurement Data leakage Android

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Diffusion Model with Multi-layer Wavelet Transform for Low-Light Image Enhancement

Diffusion Model with Multi-layer Wavelet Transform for Low-L...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Jin, Haiyan Wang, Jing Zuo, Fengyuan Su, Haonan Xiao, Zhaolin Wang, Bin Zhang, Yuanlin Xi'an University of Technology 710048 China Shaanxi Key Laboratory for Network Computing and Security Technology 710048 China

ISBN: (纸本)9798350368741

Low-light image enhancement methods based on diffusion models, though effective in improving image quality, often overrely on noise sensitivity and neglect the reconstruction deviations due to the naive up- and down-sampling operations. To address this issue, we propose a novel diffusion model, MWT-Diff, which utilizes multi-layer wavelet transforms to replace up- and down-sampling based on convolutions for extracting high-order features of different scales while mitigating representation degradations. Specifically, MWT-Diff is based on the U-Net architecture;it encodes four local features after the frequency-based down-sampling at each layer and fuses the enhanced four components during the up-sampling process. Additionally, we incorporate global refinement branches to mitigate information loss and employ efficient soft gate aggregation for feature fusion and reconstruction. Extensive quantitative and qualitative experiments demonstrate that our model achieves state-of-the-art performance on benchmark datasets. Code is available at: https://***/lalalulao/MWT-Diff. © 2025 IEEE.

关键词： Diffusion Model Low-light Image Enhancement Multi-layer Wavelet Transform Soft Gate Mechanism

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Block-gram:Mining knowledgeable features for efficiently smart contract vulnerability detection

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Digital Communications and networks 2025年第1期11卷 1-12页

作者： Xueshuo Xie Haolong Wang Zhaolong Jian Yaozheng Fang Zichun Wang Tao Li Tianjin Key Laboratory of Network and Data Security Technology TianjinChina College of Computer Science Nankai UniversityTianjinChina Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province China State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesChina

Smart contracts are widely used on the blockchain to implement complex transactions,such as decentralized applications on *** vulnerability detection of large-scale smart contracts is critical,as attacks on smart contracts often cause huge economic *** it is difficult to repair and update smart contracts,it is necessary to find the vulnerabilities before they are ***,code analysis,which requires traversal paths,and learning methods,which require many features to be trained,are too time-consuming to detect large-scale on-chain ***-based methods will obtain detection models from a feature space compared to code analysis methods such as symbol *** the existing features lack the interpretability of the detection results and training model,even worse,the large-scale feature space also affects the efficiency of *** paper focuses on improving the detection efficiency by reducing the dimension of the features,combined with expert *** this paper,a feature extraction model Block-gram is proposed to form low-dimensional knowledge-based features from ***,the metadata is separated and the runtime code is converted into a sequence of opcodes,which are divided into segments based on some instructions(jumps,etc.).Then,scalable Block-gram features,including 4-dimensional block features and 8-dimensional attribute features,are mined for the learning-based model ***,feature contributions are calculated from SHAP values to measure the relationship between our features and the results of the detection *** addition,six types of vulnerability labels are made on a dataset containing 33,885 contracts,and these knowledge-based features are evaluated using seven state-of-the-art learning algorithms,which show that the average detection latency speeds up 25×to 650×,compared with the features extracted by N-gram,and also can enhance the interpretability of the detection model.

关键词： Smart contract Bytecode&opcode Knowledgeable features Vulnerability detection Feature contribution

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Textual Data De-Privatization Scheme Based on Generative Adversarial networks 24th

Textual Data De-Privatization Scheme Based on Generative Adv...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Du, Yanning Xu, Jinnan Zhang, Yaling Wang, Yichuan Wang, Zhoukai School of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

ISBN: (纸本)9789819615506

In many fields, such as healthcare, finance, and scientific research, data sharing and collaboration are critical to achieving better outcomes. However, the sharing of personal data often involves privacy risks, so privacy-preserving techniques are needed to ensure data security and privacy. The superior performance and flexibility of generative models in data representation have led to significant progress in the development of data privacy. This paper proposes a textual data de-privatization scheme based on generative adversarial networks that well combines generative adversarial networks with privacy protection. The generated de-privacy data retains the statistical properties of the original data and effectively removes sensitive information. Moreover, feature sorting and reshaping modules are introduced to enable the generator to better capture the relationships between features, thus improving the quality of synthetic data. In this paper, the utility of synthetic data is evaluated from three aspects, including similarity assessment, privacy assessment, and model utility assessment. Experimental results show that this method achieves a good trade-off in terms of textual data privacy protection and data quality maintenance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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A Technique for Ensured Cross Chain IBC Transactions Using TPM 2

A Technique for Ensured Cross Chain IBC Transactions Using T...

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2nd International Conference on Big Data and Privacy computing, BDPC 2024

作者： Wang, YiChuan Yan, YiLiang Liu, XiaoXue Gao, Wen Wang, ZiYan School of Computer Science and Engineering Xi'an University of Technology Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China

ISBN: (纸本)9798350307597

Cross chain contact across several blockchains becomes increasingly significant in importance with the increased use of blockchain technology. However, security cannot be assured when producing equivalent outputs through cross-chain transmission because it is simple to launch an assault, and without security measures;it is simple to cause the transmission material to be stolen. This article combines TPM, IBC, and associates encryption and decryption techniques for protection to overcome this issue. We have establishes the viability and efficacy of the suggested method through testing and performance analyses. The experimental findings show that our approach can successfully preserve privacy and guarantee the security of data transfer. © 2024 IEEE.

关键词： Blockchain

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Structure-based Street Tree Extraction from Mobile Laser Scanning Point Clouds 2022

Structure-based Street Tree Extraction from Mobile Laser Sca...

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5th International Conference on Image and Graphics Processing, ICIGP 2022

作者： Hao, Wen Zuo, Zhanbin Liang, Wei School of Computer Science and Engineering Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology China Shaanxi Key Laboratory for Network Computing and Security Technology China

ISBN: (纸本)9781450395465

We present an automatic method based on structure analysis for extracting street trees from mobile laser scanning (MLS) data. Tree trunk and canopy can be characterized by the shape information and height above the ground level. Therefore, MLS point clouds are firstly divided into three layers (ground, low layer above the ground, high layer above the ground) with respect to the vertical height. For the points above the ground, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering method is applied to cluster the points into segments and geometrical features are used to extract trunk candidates and canopy candidates. Then, a "structure-matching" strategy is proposed based on minimum bounding rectangle (MBR) mapping to extract the tree candidates. Finally, a slicing method based on axial symmetry is proposed to segment the overlapped canopy. The experimental results show that the proposed method can quickly extract individual trees from massive street point clouds. © 2022 ACM.

关键词： Extraction

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Never Lost keys:A Novel key Generation Scheme Based on Motor Imagery EEG in End-Edge-Cloud System

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China Communications 2022年第7期19卷 172-184页

作者： Yichuan Wang Dan Wu Xiaoxue Liu Xinhong Hei School of computer science and Engineering Xi’an University of TechnologyXi’an 710048China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an 710048China

Biometric key is generated from the user’s unique biometric features,and can effectively solve the security problems in ***,the current prevailing biometric key generation techniques such as fingerprint recognition and facial recognition are poor in randomness and can be forged *** to the characteristics of Electroencephalographic(EEG)signals such as the randomness,nonlinear and non-stationary etc.,it can significantly avoid these *** paper proposes a novel method to generate keys based on EEG signals with end-edgecloud collaboration *** sensors to measure motor imagery EEG data,the key is generated via pre-processing,feature extraction and *** show the total time consumption of the key generation process is about *** scheme is practical and feasible,which provides a research route to generate biometric keys using EEG data.

关键词： EEG biometric key generation end-edgecloud system information security

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Locally differentially private high-dimensional data synthesis

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Science China(Information Sciences) 2023年第1期66卷 25-42页

作者： Xue CHEN Cheng WANG Qing YANG Teng HU Changjun JIANG Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University

In local differential privacy(LDP), a challenging problem is the ability to generate highdimensional data while efficiently capturing the correlation between attributes in a dataset. Existing solutions for low-dimensional data synthesis, which partition the privacy budget among all attributes, cease to be effective in high-dimensional scenarios due to the large-scale noise and communication cost caused by the high dimension. In fact, the high-dimensional characteristics not only bring challenges but also make it possible to apply some technologies to break this bottleneck. This paper presents Sam Priv Syn for high-dimensional data synthesis under LDP, which is composed of a marginal sampling module and a data generation *** marginal sampling module is used to sample from the original data to obtain two-way marginals. The sampling process is based on mutual information, which is updated iteratively to retain, as much as possible,the correlation between attributes. The data generation module is used to reconstruct the synthetic dataset from the sampled two-way marginals. Furthermore, this study conducted comparison experiments on the real-world datasets to demonstrate the effectiveness and efficiency of the proposed method, with results proving that Sam PrivSyn can not only protect privacy but also retain the correlation information between the attributes.

关键词： local differential privacy high-dimensional data synthesis sampling data privacy privacy-preserving protocols

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DP-DID: A Dynamic and Proactive Decentralized Identity System

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IEEE Transactions on Information Forensics and security 2025年 20卷 4999-5014页

作者： Yin, Jie Xiao, Yang Chen, Qian Lim, Yong zhi Liu, Xuefeng Pei, Qingqi Zhou, Jianying Xidian University State Key Laboratory of Integrated Services Network Xi’an710071 China Singapore University of Technology and Design Department of Information Systems Technology and Design Tampines 487372 Singapore Xidian University State Key Laboratory of Integrated Services Network Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China

Decentralized identity (DID) is a transformative paradigm that leverages blockchain, decentralized identifiers and verifiable credentials (VCs) to enable self-sovereign and decentralized identity management with myriad application areas. However, existing DID implementations are confronted with two key challenges: insufficient decentralization and vulnerability to mobile adversary attacks. First, they paradoxically introduce central identity resolvers, intermediaries or static committees to manage critical identity services, key management or credential issuance, which violates the decentralized controlling aim against a single point of failure. Second, these systems are vulnerable to mobile adversaries who can gradually compromise multiple nodes or committee members over a long period, eventually seizing control of the system. In this paper, we propose DP-DID, the first dynamic and proactive decentralized identity system specifically designed to resist mobile adversary attacks in dynamic committee settings. To eliminate centralized authorities, DP-DID leverages blockchain, dynamic committees and BLS (Named after Boneh, Lynn, and Shacham) signatures, which achieves decentralization. In addition, we design a dynamic and batch proactive secret sharing (DBPSS) scheme for DP-DID to ensure proactive security against mobile adversary attacks. This is achieved by allowing at most t (threshold) committees to be corrupted per period, with the set of corrupted committees changing dynamically even if all players are eventually compromised. By incorporating DBPSS, DP-DID achieves efficient key management for multiple users in dynamic settings, enhancing overall system scalability. Through rigorous analysis, DP-DID is proven to be forward secure and secure against mobile adversary attacks under a widely adopted malicious model. Extensive experiments show that DP-DID has efficient performance, and our DBPSS scheme outperforms FaB-DPSS by over 11.67× in key handover efficiency. ©

关键词： Decentralized finance

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A Pseudonym Exchange-Based Traceable Location Privacy Protection Scheme for IoV

A Pseudonym Exchange-Based Traceable Location Privacy Protec...

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2023 International Conference on networking and network Applications, NaNA 2023

作者： Ma, Weigang Yu, Yaping Wang, Yichuan Liu, Xiaoxue Wang, Zhoukai School of Computer Science and Engineering Xi'an University of Technology Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technology China

ISBN: (纸本)9798350327380

Internet of Vehicles (IoV) is a network built by communicating entities such as vehicles and roadside infrastructure, etc. The entities can communicate with each other, sharing data and providing services. However, the openness and interoperability (location services) of IoV makes the communication entity vulnerable to attacks. when the vehicle requests location services, it will expose its own location information, which will cause serious personal safety problems. To address the location privacy leakage problem in the IoV, this paper proposes a pseudonym exchange-based traceable location privacy protection scheme for IoV. Our scheme not only provides location privacy protection, but has the following properties: First, the whole pseudonym exchange process does not require trusted third-party participation;Second, each vehicle performs a series of hash operations on several dynamic anonyms and obtains a corresponding fixed-length hash value as the corresponding account in order to cut off the connection between the real identity, which is different in each run. If a vehicle is compromised and utilized to launch attack, the trusted center (CA) can also recover his/her real identity;Last, the experimental results show that the pseudonym exchange process and pseudonym update process cost less time compared to relevant literature. Hence, these features make our scheme very suitable for computation-limited mobile devices compared with other related existing schemes. © 2023 IEEE.

关键词： Vehicles

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