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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： Embedded computing Protocols Data security Computer architecture Encryption Internet Data communication

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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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Multi-party privacy-preserving decision tree training with a privileged party

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Science China(Information Sciences) 2024年第8期67卷 253-268页

作者： Yiwen TONG Qi FENG Min LUO Debiao HE Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Shanghai Technology Innovation Centre of Distributed Privacy-Preserving Artificial Intelligence Matrix Elements Technologies Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center Qilu University of Technology(Shandong Academy of Sciences)

Currently, a decision tree is the most commonly used data mining algorithm for classification tasks. While a significant number of studies have investigated privacy-preserving decision trees, the methods proposed in these studies often have shortcomings in terms of data privacy breach or efficiency. Additionally,these methods typically only apply to symmetric frameworks, which consist of two or more parties with equal privilege, and are not suitable for asymmetric scenarios where parties have unequal privilege. In this paper,we propose SecureCART, a three-party privacy-preserving decision tree training scheme with a privileged party. We adopt the existing pMPL framework and design novel secure interactive protocols for division,comparison, and asymmetric multiplication. Compared to similar schemes, our division protocol is 93.5–560.4× faster, with the communication overhead reduced by over 90%; further, our multiplication protocol is approximately 1.5× faster, with the communication overhead reduced by around 20%. Our comparison protocol based on function secret sharing maintains good performance when adapted to pMPL. Based on the proposed secure protocols, we implement SecureCART in C++ and analyze its performance using three real-world datasets in both LAN and WAN environments. he experimental results indicate that SecureCART is significantly faster than similar schemes proposed in past studies, and that the loss of accuracy while using SecureCART remains within an acceptable range.

关键词： privacy protection decision trees secure multi-party computation secret sharing privileged party

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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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Textual Data De-Privatization Scheme Based on Generative Adversarial networks 1

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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: (数字)9789819615513

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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PPEC: A Privacy-Preserving, Cost-Effective Incremental Density Peak Clustering Analysis on Encrypted Outsourced Data

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IEEE Transactions on Cloud computing 2025年第2期13卷 485-497页

作者： Yang, Haomiao Ding, Zikang Lu, Ruiheng Xiang, Kunlan Li, Hongwei Wu, Dakui University of Electronic Science and Technology of China Chengdu611731 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250353 China Shanghai University Shanghai200444 China

Call detail records (CDRs) provide valuable insights into user behavior, which are instrumental for telecom companies in optimizing network coverage and service quality. However, while cloud computing facilitates clustering analysis on a vast scale of CDR data, it introduces privacy risks. The challenge lies in striking a balance between efficiency, security, and cost-effectiveness in privacy-preserving algorithms. To tackle this issue, we propose a privacy-preserving and cost-effective incremental density peak clustering scheme. Our approach leverages homomorphic encryption and order-preserving encryption to enable direct computations and clustering on encrypted data. Moreover, it employs reaching definition analysis to optimize the execution flow of static tasks, pinpointing the optimal junctures for transitioning between the two types of encryption to reduce communication overhead. Furthermore, our scheme utilizes a game theory-based verification strategy to ascertain the accuracy of the results. This methodology can be effectively deployed on the Ethereum blockchain via smart contracts. A comprehensive security analysis confirms that our scheme upholds both privacy and data integrity. Experimental evaluations substantiate the clustering accuracy, communication load, and computational efficiency of our scheme, thereby validating its viability in real-world applications. © 2025 IEEE. All rights reserved.

关键词： Differential privacy

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A survey on cryptographic techniques for protecting big data security: present and forthcoming

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Science China(Information Sciences) 2022年第10期65卷 73-106页

作者： Siqi LU Jianhua ZHENG Zhenfu CAO Yongjuan WANG Chunxiang GU School of Cyberspace Security Information Engineering University Henan Key Laboratory of Network Cryptography Technology Institute of Security Technology Shanghai Key Laboratory of Trustworthy Computing East China Normal University Cyberspace Security Research Center Peng Cheng Laboratory Shanghai Institute of Intelligent Science and Technology Tongji University

Big data drive multidimensional convergence and profound innovations among industries and provide novel ways of exploring the world. As they significantly create economic and social value, big data meaningfully impact the implementation and management of information security and privacy *** technologies are used to protect the security and entire life cycle of big data. The demand for this technology is multiplied when the data are stored in the cloud. They are stored in the cloud in the form of ciphertext, and the driving requirement for data retrieval, sharing, and manipulation places the security of data at risk. The all-or-nothing approach of traditional cryptography systems cannot realize the release and processing of data information with flexible and increasingly fine granularity. Consequently, dealing with the relationship between privacy protection and data utilization, as well as navigating the blurry boundaries between subverting either plaintext or ciphertext, has become a research focus of the current cryptographic trend for protecting big data security. Presently, there are many studies designed to solve these ***, security requirements and source encryption mode for future big data systems and applications are elaborated. Then, focusing on the practical security and functionality of the big data life cycle, including storage, retrieval, sharing, calculation, statistical analysis, and utilization, the research being conducted based on those functions is reviewed. For each cryptographic technology that meets the requirement of each type of big data security or application, security and efficiency comments and sufficient comparison analyses of cryptography schemes or protocols are provided; moreover, the current general problems and development trends are expounded. Because the current innovative research on cryptographic technology was primarily based on the development or improvement of a single solution, the study on

关键词： big data security cryptographic techniques ciphertext-based data sharing and computing authenticated encryption functional encryption homomorphic encryption secure multi-party computing

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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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A Chain-of-Task Framework for Instruction Tuning of LLMs Based on Chinese Grammatical Error Correction 31

A Chain-of-Task Framework for Instruction Tuning of LLMs Bas...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Liu, Xinpeng Xu, Bing Yang, Muyun Cao, Hailong Zhu, Conghui Zhao, Tiejun Lu, Wenpeng Faculty of Computing Harbin Institute of Technology Harbin China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences Jinan China

ISBN: (纸本)9798891761964

Over-correction is a critical issue for large language models (LLMs) to address Grammatical Error Correction (GEC) task, esp. for Chinese. This paper proposes a Chain-of-Task (CoTask) framework to reduce over-correction. The CoTask framework is applied as multi-task instruction tuning of LLMs by decomposing the process of grammatical error analysis to design auxiliary tasks and adjusting the types and combinations of training tasks. A supervised fine-tuning (SFT) strategy is also presented to enhance the performance of LLMs, together with an algorithm for automatic dataset annotation to avoid additional manual costs. Experimental results demonstrate that our method achieves new state-of-the-art results on both FCGEC (in-domain) and NaCGEC (out-of-domain) test sets. © 2025 Association for Computational Linguistics.

关键词： Chains

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Enhancing Stance Detection on Social Media via Core Views Discovery 27

Enhancing Stance Detection on Social Media via Core Views Di...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Yan, Yu Shen, Yinghan Liu, Teli Jiang, Xuhui Yin, Dechun School of Information and Network Security People Public Security University of China China Institute of Computing Technology Chinese Academy of Sciences China Key Laboratory of AI Safety Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9781643685489

Stance detection aims to identify the expressed attitude towards a target from the text, which is significant for learning public cognition from social media. The short and implicit nature of social media users' expressions potentially results in the stance understanding bias of the model. To address this problem, introducing external background information is helpful to mitigate these biases and enhance explainability. The core view, reflecting the motivations and reasons behind an individual's stance toward the target, can be summarized and extracted from collective tweets, which can serve as a reference for stance detection. In this study, we propose the Stance Detection via Core View Discovery (SD-CVM), where the core views are used for background information modeling. Specifically, we construct a joint classifier combining the semantic understanding of tweets and their relevant core views from the public. We utilize the Large Language Model (LLM) to extract core views with stances from tweets and use these core views as background references for tweets. To further optimize the tweet understanding, we develop the contrastive and rebalancing mechanism by incorporating stance supervision signals for training. Experiments on two representative datasets demonstrate the excellent performance of our method. © 2024 The Authors.

关键词： Tweets

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