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Privacy Preserving Adjacency Query Supporting Homoionym Search over Medical Graph Data in Cloud Computing 17th

Privacy Preserving Adjacency Query Supporting Homoionym Sear...

17th International Conference on Cloud Computing, CLOUD 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Li, Hourong Tian, Yun Wu, Bin Shi, Jiaoli School of Computer and Big Data Science Jiujiang University Jiujiang332005 China Jiujiang Key Laboratory of Network and Information Security Jiujiang332005 China

ISBN: (纸本)9783031771521

Cloud computing is widely used in all walks of life today. Massive amounts of medical graph data are being outsourced to cloud servers to reduce overhead. The untrustworthiness of cloud servers puts the sensitive information of outsourced graph data at risk. To eliminate this security risk, it is an effective method to encrypt sensitive data. The adjacent queries are frequently used and highly valuable in graph data operations, and the adjacency query supporting homoionym search will enlarge the query effect and improve the query function. When the medical graph data is encrypted and stored on the cloud server, the operation of the data becomes extremely difficult. In this article, we propose a scheme to implement the adjacency query supporting homoionym search in cloud computing (AQHS), which maintains search contents privacy. We use a stem extraction algorithm and the searchable encryption mechanism to build secure index, and then achieve the adjacency query. The security of our proposed scheme is verified by formal analysis, and the effectiveness of the scheme is verified by experimental analysis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Structured Query Language

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A generalized reliability analysis of 3-ary n-cubes 16

A generalized reliability analysis of 3-ary n-cubes

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16th International Conference on Communication Software and networks, ICCSN 2024

作者： Bai, Zefang Wang, Guijuan Ma, Meijie School of Mathematics and Statistics Jinan250353 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China

ISBN: (纸本)9798331506322

Evaluating the reliability of interconnection networks is crucial. The edge-connectivity is a parameter used to assess reliability. In this paper, we discuss a conditional edge-connectivity called the h-extra g-component edge-connectivity. It combines the extra edge-connectivity and the component edge-connectivity. First, this paper proved that c λ3h(Qn3)=4nh-4eh(Qn3)-h for 1 ≤ h ≤ 3(1/2), n ≥ 3. Then, the paper given the upper bound of h-extra g-component edge-connectivity of the 3-ary n-cubes for g ∞ {4,3c3+1}, 1 ≤ c≤ [n/2] -1, n ≥ 3. © 2024 IEEE.

关键词： Interconnection networks (circuit switching)

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RFSD-YOLO: An Enhanced X-Ray Object Detection Model for Prohibited Items

RFSD-YOLO: An Enhanced X-Ray Object Detection Model for Proh...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Kong, Xiaotong Li, Aimin Li, Wenqiang Li, Zhiyao Zhang, Yuechen Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

X-ray image detection is essential for ensuring public safety, but traditional methods rely heavily on human analysis and are relatively inefficient. To address this issue, this paper proposes a new object detection model called RFSD-YOLO, which is based on the YOLOv8 model and is specifically designed for detecting prohibited items in X-ray images. The model adopts the RFCAConv structure instead of the conventional convolutional operation. This allows for independent parameterisation among the convolutional kernels, enhancing the model's ability to capture and express potential prohibited item features. The neck section of the model includes the GSConv and VoVgscspdesigns, which aim to balance complexity and parameter size while maintaining detection performance and reducing computational burden. Additionally, we have introduced a dynamic head structure, DyHead, which replaces the traditional detection head design and improves detection accuracy without adding computational cost. The experimental results demonstrate that our enhanced model surpasses the current state-of-the-art object detection models in detecting prohibited items. Additionally, we introduce a simplified version of the RFSD-YOLOnano model to cater to resource-constrained environments. This streamlined model improves AP, AR, and mAP by 4.4%, 14.6%, and 3.4%, respectively, compared to YOLOv8n. This series of innovations not only validates the superiority of our model, but also provides new solutions in the field of automated detection of prohibited items for X-ray security screening. © 2024 IEEE.

关键词： Object recognition

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CB-YOLO: A Small Object Detection Algorithm for Industrial Scenarios

CB-YOLO: A Small Object Detection Algorithm for Industrial S...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Chen, Hao Wu, Xiaoming Su, Zhanzhi Zhao, Ying Dong, Yunfeng Liu, Xiangzhi Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781665410205

In certain specific industrial scenarios, smoking and cellphone usage are strictly prohibited behaviors. In these scenarios, it is crucial to rapidly and accurately detect smoking and cell phone usage, and promptly issue warnings, to ensure industrial safety. The detection of prohibited behaviors using computer vision has gained attention from researchers with the development of artificial intelligence. However, detecting small objects like cigarettes and cell phones in complex backgrounds and varying angles poses a challenge due to their changing shapes. In this paper, we propose a detection model called CB-YOLO, which utilizes YOLOv7 as the baseline model, for detecting smoking and mobile phone usage behavior. Firstly, we propose a new channel space pyramid network called CSPPF that pools features at different scales and introduces scale focusing on improving the perceptual ability of the network to better handle targets at various scales and locations. Secondly, we propose an enhanced feature pyramid called AWBFPN. This introduces additional learnable weight parameters to improve the model's ability to fuse multi-scale features effectively. Finally, we have also proposed a new loss function called Size-IoU. The experimental results demonstrate that our algorithm outperforms the baseline model. Specifically, it achieves a 3.9% improvement in Precision, a 2.1% improvement in Recall, a 6.4% improvement in mAP@0.5, and a 2% improvement in mAP@0.5:0.95 on the Phone_check dataset. Similarly, on the Smoking dataset, our algorithm achieves a 3.3% improvement in Precision, a 1.9% improvement in Recall, a 1.7% improvement in mAP@0.5, and a 0.3% improvement in mAP@0.5:0.95. © 2024 IEEE.

关键词： Object detection

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BTP-CAResNet: An Encrypted Traffic Classification Method Based on Byte Transfer Probability and Coordinate Attention Mechanism

BTP-CAResNet: An Encrypted Traffic Classification Method Bas...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Li, Junhao Shi, Huiling Zhang, Wei Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781665410205

With the extensive application of network traffic encryption technology, the accurate and efficient classification of encrypted traffic has become a critical need for network management. Deep learning has become the predominant method for traffic classification, primarily involving the transformation of network traffic into grayscale images and their subsequent classification using Convolutional Neural networks (CNNs). However, traditional grayscale image generation methods are plagued with issues of redundant and lost information, and conventional channel attention mechanisms are still insufficient in capturing key traffic features, collectively hindering the enhancement of classification performance. To tackle these issues, this paper introduces a classification method based on Byte Transfer Probability and Coordinate Attention Mechanism in Residual network (BTP-CAResNet). This method, on the foundation of the classic ResNet architecture, incorporates a new grayscale image generation method that utilizes Byte Transfer Probability, effectively overcoming the deficiencies of traditional approaches. Additionally, this paper integrates a Coordinate Attention Mechanism into the ResNet model, which effectively overcomes the limitations of traditional channel attention mechanisms and further improves the performance of traffic classification. Experimental validation on the ISCX VPN-nonVPN dataset demonstrates that, compared to previous CNN-based methods, the method proposed in this paper exhibits superior performance in key metrics such as accuracy, precision, recall, and F1 score. It provides a new perspective for traffic classification based on convolutional neural networks. © 2024 IEEE.

关键词： Convolutional neural networks

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Privacy-Preserving Access Control Model for E-Medical Systems 2

Privacy-Preserving Access Control Model for E-Medical System...

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

作者： Cai, Rongsheng Chen, Lanxiang Zhu, Yizhao City University of Macau Faculty of Data Science 999078 China College of Computer and Cyber Security Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou China

ISBN: (纸本)9798350307597

With the high speed development of information technology, the healthcare system increasingly relies on cloud servers for data storage and complex computation, patient data privacy issues stem from healthcare systems' vulnerability to breaches, despite data encryption emphasis, insufficient focus on preventing unauthorized access risks compromises patient information confidentiality. Consequently, the need to protect privacy within the healthcare system has gained significant attentions. However, existing research primarily concentrates on data encryption processes, but there is comparatively less attention given to addressing potential privacy leaks concerning data visitors. In this paper, we propose a privacy-preserving access control model for healthcare cloud data visitors. Our model combines the RBAC (Role-Based Access Control) model with the PSI (Private Set Intersection) technique to ensure the privacy and security of data visitors. The performance analysis of our proposed solution demonstrates its relative efficiency and practicality when applied to E-medical systems. © 2024 IEEE.

关键词： Differential privacy

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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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Malware Detection Method Based on TPE Optimized Stacking 3

Malware Detection Method Based on TPE Optimized Stacking

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3rd International Conference on High Performance Computing and Communication Engineering, HPCCE 2023

作者： Zhao, Dongbo Yuan, Zesong Xie, Nannan Jilin Province Key Laboratory of Network and Information Security School of Computer Science and Technology Changchun University of Science and Technology Changchun130022 China

ISBN: (数字)9781510674653

ISBN: (纸本)9781510674646

Fast detection and categorization of malware are increasingly important for securing hosts and networks. Although many Machine Learning models have been utilized to detect malware, single-model detection may not remain efficient in the face of diverse datasets. The continuously increasing size of malware need the distributed storage systems and distributed computing. The distributed methods and ensemble learning are introduced into malware detection, and an optimal combination of base classifiers suitable for stacking are designed. A hyperparameter optimization method based on the Tree-structured Parzen Estimator (TPE) approach to enhance malware detection. The proposed method is implemented on Apache Spark and Hadoop Distributed File System(HDFS). Experiments conducted on four independent datasets, including Android and Windows, demonstrate that the proposed method can achieve 99.41% accuracy on the Android dataset and 96.96% accuracy on the Windows dataset. © 2024 SPIE.

关键词： Learning systems

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Conditional selection with CNN augmented transformer for multimodal affective analysis

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 917-931页

作者： Jianwen Wang Shiping Wang Shunxin Xiao Renjie Lin Mianxiong Dong Wenzhong Guo College of Computer and Data Science Fuzhou UniversityFuzhouChina College of Computer and Cyber Security Fujian Normal UniversityFuzhouChina Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhouChina Digital Fujian Institute of Big Data Security Technology Fujian Normal UniversityFuzhouChina Department of Sciences and Informatics Muroran Institute of TechnologyMuroranJapan

Attention mechanism has been a successful method for multimodal affective analysis in recent years. Despite the advances, several significant challenges remain in fusing language and its nonverbal context information. One is to generate sparse attention coefficients associated with acoustic and visual modalities, which helps locate critical emotional se-mantics. The other is fusing complementary cross‐modal representation to construct optimal salient feature combinations of multiple modalities. A Conditional Transformer Fusion network is proposed to handle these problems. Firstly, the authors equip the transformer module with CNN layers to enhance the detection of subtle signal patterns in nonverbal sequences. Secondly, sentiment words are utilised as context conditions to guide the computation of cross‐modal attention. As a result, the located nonverbal fea-tures are not only salient but also complementary to sentiment words directly. Experi-mental results show that the authors’ method achieves state‐of‐the‐art performance on several multimodal affective analysis datasets.

关键词： affective computing data fusion information fusion multimodal approaches

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Privacy-Preserving Responsible Gaming Systems 2

Privacy-Preserving Responsible Gaming Systems

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

作者： Zhu, Yizhao Chen, Lanxiang City University of Macau Faculty of Data Science 999078 China College of Computer and Cyber Security Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou China

ISBN: (纸本)9798350307597

Responsible gaming aims at maintaining the integrity and sustainability of the gaming industry. However, The exchange of sensitive data, such as player and gaming related information, among different parties raises significant privacy concerns, and safeguarding privacy becomes a top priority consideration throughout the design and implementation of responsible gaming systems. In this paper, we introduce an innovative scheme that utilizes Oblivious Transfer (OT) and Private Set Intersection (PSI) protocols to address privacy concerns of responsible gaming systems. Our scheme proposes a system model that addresses privacy concerns related to player global limits and banned player list query and response, without requiring infrastructure changes, unlike blockchain-based solutions;Our proposed scheme not only addresses privacy concerns between the query user and the centralized database server but also mitigates privacy concerns between the query user and certificate authority (CA) by incorporating blind RSA signature scheme;Our proposed scheme achieves linear complexity in computation and communication, while remaining resilient against malicious adversaries under the random oracle model. © 2024 IEEE.

关键词： Differential privacy

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