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TPTVer： A Trusted Third Party Based Trusted Verifier for Multi-Layered Outsourced Big Data System in Cloud Environment

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China Communications 2018年第2期15卷 122-137页

作者： Jing Zhan Xudong Fan Lei Cai Yaqi Gao Junxi Zhuang College of Computer Science Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Trusted Computing Beijing China National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing China

Cloud computing is very useful for big data owner who doesn't want to manage IT infrastructure and big data technique details. However, it is hard for big data owner to trust multi-layer outsourced big data system in cloud environment and to verify which outsourced service leads to the problem. Similarly, the cloud service provider cannot simply trust the data computation applications. At last,the verification data itself may also leak the sensitive information from the cloud service provider and data owner. We propose a new three-level definition of the verification, threat model, corresponding trusted policies based on different roles for outsourced big data system in cloud. We also provide two policy enforcement methods for building trusted data computation environment by measuring both the Map Reduce application and its behaviors based on trusted computing and aspect-oriented programming. To prevent sensitive information leakage from verification process,we provide a privacy-preserved verification method. Finally, we implement the TPTVer, a Trusted third Party based Trusted Verifier as a proof of concept system. Our evaluation and analysis show that TPTVer can provide trusted verification for multi-layered outsourced big data system in the cloud with low overhead.

关键词： big data security outsourced ser-vice security MapReduce behavior trustedverification trusted third party

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Exfiltration-resistant Proxy Re-Encryption for IoT Data Sharing in Unreliable Clouds

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Zhou, Yuyang Zhao, Liang Li, Fagen Takagi, Tsuyoshi School of Cyberspace Security Hainan University Haikou570000 China Department of Mathematical Informatics Graduate School of Information Science and Technology The university of Tokyo Tokyo113-8656 Japan School of Cyber Science and Engineering Sichuan University Chengdu610065 China Key Laboratory of Data Protection and Intelligent Management [Sichuan University Ministry of Education China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

The sharing of Internet of Things (IoT) data plays an extensive role in our everyday lives. Exploring secure and efficient methods for data sharing is a prominent area of research. Proxy re-encryption (PRE) in the cloud provides a solution. However, traditional PRE schemes are vulnerable to algorithm substitution attacks (ASA). Moreover, in most PRE schemes, the proxy can know the relevant identity information of the data owner or the data recipient through the re-encryption key, and some current PRE schemes cannot guarantee strong resistance to collision. To address the limitations of traditional PRE schemes and offer a robust solution for secure and efficient data sharing in IoT application, we propose a reverse firewall for proxy re-encryption (PRE-RF) scheme, which we implement using the JPBC library. The security analysis reveals that the PRE-RF scheme offers effective resistance against ASA, while also providing strong collision security, chosen plaintext attack security, and key-private security. Compared to similar PRE schemes with more security features, our scheme reduces the length of the re-encryption keys and ciphertext, thereby reducing the storage cost of the system;also, our scheme’s computational cost is lower, thereby improving the operational efficiency of the system. Furthermore, as security levels rise, the portion of time dedicated to reverse firewall operations within our scheme diminishes. These advantages collectively render it a secure and efficient choice for sharing IoT data in unreliable cloud environments. © 2004-2012 IEEE All rights reserved.

关键词： Ciphertext

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Cross-Layer security for Semantic Communications: Metrics and Optimization

arXiv

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arXiv 2025年

作者： Wang, Lingyi Wu, Wei Zhou, Fuhui Qin, Zhijin Wu, Qihui College of Science Nanjing University of Posts and Telecommunications Nanjing210003 China College of Communication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing210000 China Department of Electronic Engineering Tsinghua University Beijing100084 China Beijing National Research Center for Information Science and Technology Beijing China The State Key Laboratory of Space Network and Communications Beijing China

Different from traditional secure communication that focuses on symbolic protection at the physical layer, semantic secure communication requires further attention to semantic-level task performance at the application layer. There is a research gap on how to comprehensively evaluate and optimize the security performance of semantic communication. In order to fill this gap, a unified semantic security metric, the cross-layer semantic secure rate (CLSSR), is defined to estimate cross-layer security requirements at both the physical layer and the application layer. Then, we formulate the maximization problem of the CLSSR with the mixed integer nonlinear programming (MINLP). We propose a hierarchical AI-native semantic secure communication network with a reinforcement learning (RL)based semantic resource allocation scheme, aiming to ensure the cross-layer semantic security (CL-SS). Finally, we prove the convergence of our proposed intelligent resource allocation, and the simulation results demonstrate that our proposed CLSS method outperforms the traditional physical layer semantic security (PL-SS) method in terms of both task reliability and CLSSR. Copyright © 2025, The Authors. All rights reserved.

关键词： Mixed-integer linear programming

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A trusted behaviour measurement scheme based on feedback and control in trusted network

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International Journal of Wireless and Mobile Computing 2021年第2期20卷 99-106页

作者： Zhuang, Junxi Zhao, Xu Gong, Bei Zhan, Jing College of Computer Science Beijing Key Laboratory of Trusted Computing Beijing University of Technology Beijing100124 China National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing University of Technology Beijing100124 China

To solve the real-time trusted measurement of user behaviour in trusted networks, a new combination of dynamic and static, subjective and objective behaviour trusted measurement scheme is presented. The scheme analyses the credibility of the real-time user state and the direct trusted measurement value of the user behaviour, and finally controls the user behaviour by the feedback measurement of the user behaviour. The research results show that when the ratio of untrusted nodes is 15%, 30% and 50%, the trusted detection rate of this paper can reach more than 92%, and the untrusted node in trusted network can be identified with the increase of time. Compared with traditional measurement scheme, the scheme can be more accurate and reasonable to calculate the credibility of user behaviour, and can reflect the changes of the credibility of users’ actual behaviour at all times, and ultimately implement the feedback control on user behaviour. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Feedback control

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Construction of Integrated protection System for Industrial Control System Based on Trusted Computing

Construction of Integrated Protection System for Industrial ...

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2021 International Conference on Intelligent Computing, Automation and Applications, ICAA 2021

作者： Tao, Yuan Hu, Wei Li, Sheng Third Research Institute of Ministry of Public Security Shanghai China National Engineering Laboratory for Key Technology of Classified Information Security Protection Beijing China

ISBN: (纸本)9781665437301

Industrial control system requires high availability. In principle, security measures should not adversely affect the basic functions of high availability industrial control system. The method of building a secure and trusted PLC are proposed based on trusted computing. Under the premise of ensuring business security, the security protection system of industrial control system is built through trusted computing, and hardware solutions are provided on the security master controller. In order to prevent the attack from the industrial control system network, the encryption technology is used to ensure the confidentiality and integrity of the communication session, which are between the field of control equipment layer and the process monitoring equipment layer, so as to make the industrial control system in a stable and reliable state. © 2021 IEEE.

关键词： Trusted computing

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Ciphertext-Policy Attribute Based Encryption Supporting Any Monotone Access Structures Without Escrow

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Chinese Journal of Electronics 2017年第3期26卷 640-646页

作者： ZHAO Qianqian ZHANG Yuqing ZHANG Guanghua WANG He State Key Laboratory of Integrated Services Networks Xidian University National Computer Network Intrusion Protection Center University of Chinese Academy of Sciences Beijing Key Laboratory of IOT Information Security Technology Institute of Information Engineering CAS

Ciphertext policy attribute-based encryption(CP-ABE)is becoming a new primitive for finegrained access *** neither produces multiple encrypted copies of the same data nor suffers from the severe burden of key distribution and *** escrow problem that the central authority could decrypt any ciphertexts addressed to all the specific users is still a challenge for CP-ABE *** new CP-ABE scheme without escrow is proposed,and furthermore the proposed scheme achieves fully security in the standard *** performance and security analysis results indicate that the proposed CP-ABE scheme is extremely appropriate for cloud storage system.

关键词： CP-ABE The escrow problem Fully security In the standard model

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A novel image encryption method based on hyperchaotic system and pixel information association

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International Journal of Wireless and Mobile Computing 2018年第3期15卷 207-214页

作者： Duan, Lijuan Zhang, Dongkui Ning, Zhenhu Xu, Fan Cui, Guoqin Liang, Peng Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Trusted Computing National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing100124 China State Key Laboratory of Digital Multi-Media Chip Technology Vimicro Corporation Beijing100191 China State Information Center National Engineering Laboratory for e-Government Integration and Application Beijing100045 China

In recent years, a variety of image encryption methods based on chaotic systems has been proposed, and has achieved good results. In order to improve the security of image encryption, this paper proposes a new image encryption method with a dynamic key, based on a hyperchaotic system and the association of pixel information, and designs the three-level encryption structure with diffusion encryption, scrambling encryption and re-diffusion encryption. The innovation of this method is to add the whole information of the image and the position information of the pixels in the diffusion encryption, and add the data information of the pixels in the scrambling encryption. The plaintext information of the image is added to the encryption process, which can enhance the security of encryption. Experiments demonstrate that the image encryption method proposed in this paper has the characteristics of strong sensitivity, low correlation between adjacent pixels, anti-differential attack and high security. Copyright © 2018 Inderscience Enterprises Ltd.

关键词： Cryptography

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Learning EEG synchronization patterns for epileptic seizure prediction using bag-of-wave features

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Journal of Ambient Intelligence and Humanized Computing 2023年第11期14卷 15557-15572页

作者： Cui, Song Duan, Lijuan Qiao, Yuanhua Xiao, Ying Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Trusted Computing National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing100124 China College of Applied Sciences Beijing University of Technology Beijing100124 China

Epileptic seizure prediction has the potential to promote epilepsy care and treatment. However, the seizure prediction accuracy does not satisfy the application requirements. In this paper, a novel framework for seizure prediction is proposed by learning synchronization patterns. For better representation, bag-of-wave (BoWav) feature extraction is proposed for modeling synchronization pattern of electroencephalogram (EEG) signal. An interictal codebook and preictal codebook, representing the local segments, are constructed by a clustering algorithm. Within a period of EEG signal on all electrodes, local segments are projected onto the learned codebooks. The proposed feature expresses the synchronization pattern of EEG signal with the histogram feature. Moreover, extreme learning machine (ELM) is used to classify the sequence of features. Experiments are performed on the Kaggle seizure prediction challenge dataset and the CHB-MIT dataset. The experiment on the CHB-MIT achieves a sensitivity of 88.24% and a false prediction rate per hour of 0.25. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Feature extraction

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EEG Source Localization Using Spatio-Temporal Neural Network

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China Communications 2019年第7期16卷 131-143页

作者： Song Cui Lijuan Duan Bei Gong Yuanhua Qiao Fan Xu Juncheng Chen Changming Wang Faculty of Information Technology Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Trusted Computing National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing 100124 China College of Applied Sciences Beijing University of Technology Beijing 100124 China The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders Beijing Anding Hospital Capital Medical University Beijing 100088 China Advanced Innovation Center for Human Brain Protection Capital Medical University Beijing 100069 China

Source localization of focal electrical activity from scalp electroencephalogram (sEEG) signal is generally modeled as an inverse problem that is highly ill-posed. In this paper, a novel source localization method is proposed to model the EEG inverse problem using spatio-temporal long-short term memory recurrent neural networks (LSTM). The network model consists of two parts, sEEG encoding and source decoding, to model the sEEG signal and receive the regression of source location. As there does not exist enough annotated sEEG signals correspond to specific source locations, simulated data is generated with forward model using finite element method (FEM) to act as a part of training signals. A framework for source localization is proposed to estimate the source position based on simulated training data. Experiments are done on simulated testing data. The results on simulated data exhibit good robustness on noise signal, and the proposed network solves the EEG inverse problem with spatio-temporal deep network. The result show that the proposed method overcomes the highly ill-posed linear inverse problem with data driven learning.

关键词： electroencephalogram LSTM source localization spatio-temporal modeling

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A federated sleep staging method based on adaptive re-aggregation and double prototype-contrastive using single-channel electroencephalogram

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engineering Applications of Artificial Intelligence 2025年 156卷

作者： Bian Ma Lijuan Duan Zhaoyang Huang Yuanhua Qiao College of Computer Science Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Trusted Computing Beijing 100124 China China National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing 100124 China Department of Neurology Xuanwu Hospital Capital Medical University Beijing 100053 China Beijing Key Laboratory of Neuromodulation Beijing 100053 China School of Mathematics Statistics and Mechanics Beijing University of Technology Beijing 100124 China

The limited availability of sleep data collected by individual institutions poses a significant challenge in developing automatic sleep staging models, especially given that most existing models rely heavily on data-driven approaches. The advent of federated learning has introduced an innovative and reliable paradigm for inter-institutional collaboration, facilitating task knowledge sharing and implicit data augmentation while preserving privacy. However, in traditional federated learning methodologies, local models are typically initialized with the global model at the onset of each iteration. This global-knowledge-centric approach often results in catastrophic forgetting during local training and inadequately adapts to the diversity of local data, compromising the overall generalization performance of the model. In this paper, we introduce a novel re-aggregation strategy for local model initialization that synergizes the global model with historical local models, thereby mitigating the undue influence of global knowledge and preserving local task-specific information. The re-aggregation weights are adaptively determined based on the confidence of the global and historical models on local datasets. Furthermore, to address the inherent ambiguity among sleep stages, a dual prototype-contrastive learning module is proposed, comprising Prototype-Consistency Contrastive (PCC) and Prototype-Distinguishability Contrastive (PDC) components. Specifically, the PCC component is designed to ensure consistency between local prototypes and unbiased prototypes derived from re-aggregation, enhancing intra-class knowledge coherence. The goal of the PDC component is to strengthen the discriminability among various sleep stages, improving inter-class differentiation. Comprehensive experiments conducted on two public datasets demonstrate the effectiveness, superiority, and flexibility of the proposed method.

关键词： Federated learning

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