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A new method of constructing adversarial examplesfor quantum variational circuits

Chinese Physics B 2023年第7期32卷 268-272页

作者：颜金歌闫丽丽张仕斌 School of Cybersecurity Chengdu University of Information TechnologySichuan 610000China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Sichuan 610000China

A quantum variational circuit is a quantum machine learning model similar to a neural network.A crafted adversarial example can lead to incorrect results for the *** adversarial examples to train the model will greatly improve its *** existing method is to use automatic differentials or finite difference to obtain a gradient and use it to construct adversarial *** paper proposes an innovative method for constructing adversarial examples of quantum variational *** this method,the gradient can be obtained by measuring the expected value of a quantum bit respectively in a series quantum *** method can be used to construct the adversarial examples for a quantum variational circuit *** implementation results prove the effectiveness of the proposed *** with the existing method,our method requires fewer resources and is more efficient.

关键词： quantum variational circuit adversarial examples quantum machine learning quantum circuit

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A backdoor attack against quantum neural networks with limited information

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Chinese Physics B 2023年第10期32卷 219-228页

作者：黄晨猗张仕斌 College of Cyberspace Security Chengdu University of Information TechnologyChengdu 610225China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu 610225China

Backdoor attacks are emerging security threats to deep neural *** these attacks,adversaries manipulate the network by constructing training samples embedded with backdoor *** backdoored model performs as expected on clean test samples but consistently misclassifies samples containing the backdoor trigger as a specific target *** quantum neural networks(QNNs)have shown promise in surpassing their classical counterparts in certain machine learning tasks,they are also susceptible to backdoor ***,current attacks on QNNs are constrained by the adversary's understanding of the model structure and specific encoding *** the diversity of encoding methods and model structures in QNNs,the effectiveness of such backdoor attacks remains *** this paper,we propose an algorithm that leverages dataset-based optimization to initiate backdoor attacks.A malicious adversary can embed backdoor triggers into a QNN model by poisoning only a small portion of the *** victim QNN maintains high accuracy on clean test samples without the trigger but outputs the target label set by the adversary when predicting samples with the ***,our proposed attack cannot be easily resisted by existing backdoor detection methods.

关键词： backdoor attack quantum artificial intelligence security quantum neural network variational quantum circuit

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Enhanced Privacy-Preserving WiFi Fingerprint Localization from CL Encryption

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Chinese Journal of Electronics 2024年第6期33卷 1435-1446页

作者： Zhiwei WANG Qiuchi ZHU Zhenqi ZHANG School of Computer Nanjing University of Posts and Telecommunications State Key Laboratory of Public Big Data Guizhou University Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu University of Information Engineering Key Laboratory of Computational Science and Application of Hainan Province Hainan Normal University

The WiFi fingerprint-based localization method is considered one of the most popular techniques for indoor localization. In INFOCOM'14, Li et al. proposed a wireless fidelity(WiFi) fingerprint localization system based on Paillier encryption, which is claimed to protect both client C 's location privacy and service provider S's database privacy. However, Yang et al. presented a practical data privacy attack in INFOCOM'18, which allows a polynomial time attacker to obtain S's database. We propose a novel WiFi fingerprint localization system based on CastagnosLaguillaumie(CL) encryption, which has a trustless setup and is efficient due to the excellent properties of CL encryption. To prevent Yang et al.'s attack, the system requires that S selects only the locations from its database that can receive the nonzero signals from all the available access points in C 's nonzero fingerprint in order to determine C's location. security analysis shows that our scheme is secure under Li et al.'s threat model. Furthermore, to enhance the security level of privacy-preserving WiFi fingerprint localization scheme based on CL encryption, we propose a secure and efficient zero-knowledge proof protocol for the discrete logarithm relations in C's encrypted localization queries.

关键词： Location awareness Threat modeling Data privacy Protocols Databases Fingerprint recognition Trustless services Encryption Servers Wireless fidelity

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Variational Quantum Isometric Feature Mapping 4

Variational Quantum Isometric Feature Mapping

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4th International Symposium on Computer Technology and Information Science, ISCTIS 2024

作者： Gong, Guoqing Chengdu University of Information Technology Advanced Cryptography and System Security Key Laboratory of Sichuan Province School of Cybersecurity Sichuan China

ISBN: (数字)9798350354560

ISBN: (纸本)9798350354560

Currently, big data often encounters "curse of dimensionality"problems. Using classic nonlinear dimensionality reduction algorithm, the complexity increases sharply with the increase of data dimension and quantity. While, quantum dimensionality reduction algorithm can achieve exponential acceleration for large-scale data processing, it requires a large number of n -qubit operations. To solve the problems above, this paper proposed a variational quantum isometric feature mapping algorithm based on classical nonlinear dimensionality reduction algorithm, quantum machine learning algorithm and variational quantum algorithm. In the data processing stage, the quantum k-nearest neighbor algorithm is used to obtain the K nearest neighbors of the original data, construct the adjacency graph, and calculate the geodesic distance between each point to get the distance matrix. The inner product matrix is obtained by redesigning the quantum circuit. Then the characteristic problem of the inner product matrix is solved by using the variational quantum eigensolver. Finally, the representation of low-dimensional data after dimensionality reduction is obtained, and the nonlinear dimensionality reduction of large-scale high-dimensional data is realized. © 2024 IEEE.

关键词： Dimensionality reduction

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A Cross-Multi-Domain Trust Assessment Authority Delegation Method Based on Automotive Industry Chain

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Computers, Materials & Continua 2025年第1期82卷 407-426页

作者： Binyong Li Liangming Deng Jie Zhang Xianhui Deng School of Cybersecurity(Xin Gu Industrial College) Chengdu University of Information TechnologyChengdu610225China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225China SUGON Industrial Control and Security Center Chengdu610225China

To solve the challenges of connecting and coordinating multiple platforms in the automotive industry and to enhance collaboration among different participants,this research focuses on addressing the complex supply relationships in the automotive market,improving data sharing and interactions across various platforms,and achieving more detailed integration of data and *** propose a trust evaluation permission delegation method based on the automotive industry *** proposed method combines smart contracts with trust evaluation mechanisms,dynamically calculating the trust value of users based on the historical behavior of the delegated entity,network environment,and other factors to avoid malicious node attacks during the permission delegation *** also introduce strict control over the cross-domain permission granting and revocation mechanisms to manage the delegation path,prevent information leakage caused by malicious node interception,and effectively protect data integrity and *** analysis shows that this method meets the realtime requirements of collaborative interaction in the automotive industry chain and provides a feasible solution to permission delegation issues in the automotive industry chain,offering dynamic flexibility in authorization and scalability compared to most existing solutions.

关键词： Automotive industry chain cross-domain access block chain delegate authority trust assessment

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Quantum private comparison of arbitrary single qubit states based on swap test

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Chinese Physics B 2022年第4期31卷 123-132页

作者： Xi Huang Yan Chang Wen Cheng Min Hou Shi-Bin Zhang School of Cybersecurity Chengdu University of Information TechnologyChengdu 610225China Sichuan Key Laboratory of Advanced Cryptography and System Security Chengdu 610225China

By using swap test,a quantum private comparison(QPC) protocol of arbitrary single qubit states with a semi-honest third party is *** semi-honest third party(TP) is required to help two participants perform the *** can record intermediate results and do some calculations in the whole process of the protocol execution,but she cannot conspire with any of *** the process of comparison,the TP cannot get two participants’ private information except the comparison *** to the security analysis,the proposed protocol can resist both outsider attacks and participants’ *** with the existing QPC protocols,the proposed one does not require any entanglement swapping technology,but it can compare two participants’ qubits by performing swap test,which is easier to implement with current ***,the proposed protocol can compare secret *** encodes secret integers into the amplitude of quantum state rather than transfer them as binary representations,and the encoded quantum state is compared by performing the swap ***,the proposed QPC protocol is extended to the QPC of arbitrary single qubit states by using multi-qubit swap test.

关键词： quantum private comparison arbitrary single qubit states swap test quantum cryptography

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Point cloud classification method based on local dynamic edge graph convolution and global information fusion 4

Point cloud classification method based on local dynamic edg...

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4th International Conference on Computer Vision, Application, and Algorithm, CVAA 2024

作者： Du, Jie Sheng, Zhiwei Jin, Xingyu Huang, Yuanyuan Tian, Junbiao Chengdu University of Information Technology Chengdu610225 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

The development of automatic driving, unmanned aerial vehicle detection and virtual reality cannot be separated from the research of 3D point cloud. Existing studies either lack of local shape capture and local spatial feature description of point cloud, resulting in poor accuracy of point cloud classification network, or pursue fine and complex local feature extractors, sacrificing extraction time and memory overhead to extract classification progress, but the results show that the effect is not significantly improved. Therefore, this paper proposes a Point cloud classification method based on Local Point Dynamic Edge Graph Convolution and Global Information Fusion. Among them, the local point dynamic edge graph convolution module improves the edge graph convolution, which can improve the local extraction feature effect without deepening the local feature extractor, and reduce the time and memory overhead. The global information fusion block concatenates multi-layer local features to extract and fuse feature information to improve the classification accuracy of the network. Our network is tested on the public datasets ModelNet40 dataset and ScanObjectNN dataset. The results show that compared with the current mainstream point cloud classification algorithms, the point cloud classification accuracy of the proposed classification method is improved, and it has good robustness. © 2025 SPIE.

关键词： Point clouds Feature extraction Convolution Data modeling Information fusion Data conversion Education and training Transformers Deep learning Statistical modeling

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Quantum Fuzzy Regression Model for Uncertain Environment

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Computers, Materials & Continua 2023年第5期75卷 2759-2773页

作者： Tiansu Chen Shi bin Zhang Qirun Wang Yan Chang School of Cybersecurity Chengdu University of Information TechnologyChengdu610225China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610255China School of Engineering and Technology University of HertfordshireHertfordUK

In the era of big data,traditional regression models cannot deal with uncertain big data efficiently and *** order to make up for this deficiency,this paper proposes a quantum fuzzy regression model,which uses fuzzy theory to describe the uncertainty in big data sets and uses quantum computing to exponentially improve the efficiency of data set preprocessing and parameter *** this paper,data envelopment analysis(DEA)is used to calculate the degree of importance of each data ***,Harrow,Hassidim and Lloyd(HHL)algorithm and quantum swap circuits are used to improve the efficiency of high-dimensional data matrix *** application of the quantum fuzzy regression model to smallscale financial data proves that its accuracy is greatly improved compared with the quantum regression ***,due to the introduction of quantum computing,the speed of dealing with high-dimensional data matrix has an exponential improvement compared with the fuzzy regression *** quantum fuzzy regression model proposed in this paper combines the advantages of fuzzy theory and quantum computing which can efficiently calculate high-dimensional data matrix and complete parameter estimation using quantum computing while retaining the uncertainty in big ***,it is a new model for efficient and accurate big data processing in uncertain environments.

关键词： Big data fuzzy regression model uncertain environment quantum regression model

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Summarizing Videos using the Uniqueness of Video Frames that Mask Adjacent Frame Information 19

Summarizing Videos using the Uniqueness of Video Frames that...

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19th International Conference on Natural Computation, Fuzzy systems and Knowledge Discovery, ICNC-FSKD 2023

作者： Huang, Yongxin Zhou, Yimin Wang, Zuli Chen, Ding Shi, Lei Chengdu University of Information Technology Chengdu610225 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province China

ISBN: (纸本)9798350304398

Video summarization can help people retrieve videos quickly. Existing unsupervised video summarization models make insufficient use of video frame uniqueness in the self-attention mechanism and have redundancy in the computation of uniqueness. In this paper, we rethink the relationship between attentive uniqueness and diversity of video frames, improve the calculation method of video frame uniqueness by masking adjacent frame information in the model framework based on a self-attention mechanism, and simplify the concentrated attention mechanism with it. In addition, we propose a new loss function called Deviation loss, which uses the uniqueness of video frames to calculate and helps train video summarization models to obtain diverse representations. The experimental results show that the proposed method outperforms other state-of-the-art unsupervised summarization approaches on the SumMe dataset, and also demonstrates competitiveness on the TVSum dataset. © 2023 IEEE.

关键词： Video recording

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Study on the Adversarial Sample Generation Algorithm Based on Adversarial Quantum Generation Adversarial Network 3

Study on the Adversarial Sample Generation Algorithm Based o...

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3rd International Symposium on Computer Technology and Information Science, ISCTIS 2023

作者： Cheng, Wen Zhang, ShiBin Lin, Yusheng School of Cybersecurity Sichuan Key Laboratory of Advanced Cryptography and System Security Chengdu University of Information Technology Chengdu610225 China

ISBN: (纸本)9798350325386

In response to the inefficiency of the existing advGAN method, which requires calculating the differences between the adversarial samples and the normal samples one by one when generating adversarial perturbations, this paper proposes a quantum adversarial sample generation algorithm (QASGA) based on adversarial quantum generative adversarial networks. First, the real samples are encoded into quantum states, then the generator G of QGAN is used to generate adversarial perturbations, which are superimposed with normal samples to obtain adversarial samples. At the same time, the SWAP-TEST method is used to calculate the similarity between all real samples and adversarial samples at once, thereby accelerating adversarial attacks. Experimental results show that the QASGA algorithm proposed in this paper can generate high-fidelity adversarial samples with a one-time similarity calculation on the IRIS dataset, and can reduce the classification accuracy of the BP neural network from 96.67% to 26.67%, verifying the effectiveness of the proposed QASGA algorithm. © 2023 IEEE.

关键词： Generative adversarial networks

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