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检索条件"任意字段=10th IEEE Conference on Big Data Security on Cloud, BigDataSecurity 2024"

共 166 条记录，以下是1-10 订阅

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Proceedings - 2024 ieee 10th conference on big data security on cloud, bigdatasecurity 2024

Proceedings - 2024 IEEE 10th Conference on Big Data Security...

引用

10th ieee conference on big data security on cloud, bigdatasecurity 2024

ISBN: (纸本)9798350389524

the proceedings contain 30 papers. the topics discussed include: a low-cost positioning beacon in rail transit system;user dual intents graph modeling for information diffusion prediction;precision assistance for residents with subsistence allowance: cluster analysis of mixed-type data based on K-prototypes;research on production capacity utilization index based on electricity big data;sarcasm detection in Chinese and English text with fine-tuned large language models;temporal analysis and prediction of marriage registration figures based on long-term time series models;and test case recommendation for blockchain systems based on graph convolutional networks.

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Proceedings - 15th ieee International conference on Trust, security and Privacy in Computing and Communications, 10th ieee International conference on big data Science and Engineering and 14th ieee International Symposium on Parallel and Distributed Processing with Applications, ieee TrustCom/bigdataSE/ISPA 2016

Proceedings - 15th IEEE International Conference on Trust, S...

引用

Joint 15th ieee International conference on Trust, security and Privacy in Computing and Communications, 10th ieee International conference on big data Science and Engineering and 14th ieee International Symposium on Parallel and Distributed Processing with Applications, ieee TrustCom/bigdataSE/ISPA 2016

ISBN: (纸本)9781509032051

the proceedings contain 311 papers. the topics discussed include: a robust image hashing with enhanced randomness by using random walk on zigzag blocking;securing fast learning! ridge regression over encrypted big data;computational trust model for repeated trust games;formal analysis of selective disclosure attribute-based credential system in applied Pi calculus;node trust prediction framework in mobile ad hoc networks;trust enhancement over range search for encrypted data;healthcare fraud detection based on trustworthiness of doctors;integrated security for services hosted in virtual environments;a permissioned blockchain framework for supporting instant transaction and dynamic block size;trust validation of cloud IaaS: a customer-centric approach;distributed bitcoin account management;trusted Boolean search on cloud using searchable symmetric encryption;and dynamic attribute-based access control in cloud storage systems.

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An Education Program for big data security and Privacy 10

An Education Program for Big Data Security and Privacy

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10th ieee conference on big data security on cloud, bigdatasecurity 2024

作者： thuraisingham, Bhavani Nimon, Kim Khan, Latifur The University of Texas Dallas United States The University of Texas Tyler United States

ISBN: (纸本)9798350389524

this paper describes the course that we developed at the University of Texas at Dallas on big data security and Privacy with funding from the National Science Foundation. the project started in 2017 and was completed in 2023. We have taught the course starting Fall 2020, Fall 2021, Fall 2022 and we are teaching it for the fourth time in Fall 2023. this is an extremely popular course and we limit the enrollment to 80 students. Students have some background in cyber security and big data analytics. this paper provides an overview of the organization of the course, the course content, and learning outcomes and the evaluation carried out. the members of the project include those who have expertise in (i) Cyber security, (ii) big data Analytics, and (iii) Education Research. © 2024 ieee.

关键词： big data

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A Distributed Attribute-Based Proxy Re-Encryption Scheme for data Sharing in IoT 10

A Distributed Attribute-Based Proxy Re-Encryption Scheme for...

引用

10th ieee conference on big data security on cloud, bigdatasecurity 2024

作者： Li, Shizhong Zhang, Wei Chen, Ruonan Li, Dawei Beihang University School of Cyber Science and Technology Beijing China Ministry of Industry and Information Technology of the People's Republic of China Industry Development and Promotion Center China

ISBN: (纸本)9798350389524

Attribute-based encryption (ABE) offers finegrained access control in the Internet of things but also imposes significant performance overhead on devices. Attribute-based proxy re-encryption (AB-PRE) serves as an elegant primitive to address this issue. In traditional schemes, the re-encryption operation is performed by a third-party proxy, which can lead to centralization and single points of failure. therefore, a distributed attribute-based proxy re-encryption scheme is proposed. the reencryption operations can be delegated to the blockchain nodes through smart contracts, significantly reducing the burden on the devices. Furthermore, data users can quickly verify the correctness of re-encrypted ciphertexts using an algorithm that incurs a constant computation cost. © 2024 ieee.

关键词： Smart contract

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A Method for Anomaly Detection in Power Time Series data within Energy big data Based on Generative Adversarial Networks 10

A Method for Anomaly Detection in Power Time Series Data wit...

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10th ieee conference on big data security on cloud, bigdatasecurity 2024

作者： Jiang, Chen Chen, Mengxian Xie, Huasen Wang, Hua Xu, Jun State Grid Wenzhou Electric Power Supply Company of Zhejiang Power Corporation Wenzhou China Hangzhou Sinuokai Technology Co. Ltd. Hangzhou China

ISBN: (纸本)9798350389524

Anomaly detection in power time series data plays a crucial role in ensuring the stability and security of energy systems. In this study, a novel approach based on Generative Adversarial Networks (GANs) is proposed to tackle this problem. Firstly, the power time series data are preprocessed to remove noise and outliers. then, a GAN model is constructed for generating synthetic power data that follow the same statistical properties as the original data. By comparing the real and synthetic data, anomalies can be identified based on their deviations. Experimental results on a real-world power dataset demonstrate the effectiveness and robustness of the proposed method. Experimental results show that the anomaly detection model has strong performance in judging data with large differences from the original data Moreover, the proposed method shows promising potential for application in other domains beyond power systems. © 2024 ieee.

关键词： big data

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Research on Production Capacity Utilization Index Based on Electricity big data 10

Research on Production Capacity Utilization Index Based on E...

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10th ieee conference on big data security on cloud, bigdatasecurity 2024

作者： Zhang, Mengyuan Zhao, Yingying Tian, Yingjie State Grid Shanghai Electric Power Research Institute Shanghai200437 China State Grid Shanghai Electric Power Research Institute Another Department Shanghai200437 China

ISBN: (纸本)9798350389524

this study investigates the capacity utilization index in Shandong Province's industrial sector by leveraging multisource electricity data and external data to align with national socioeconomic priorities and the State Grid's strategic direction. It focuses on ten representative industries, revealing unique load profiles through advanced data modeling. A novel capacity utilization index is constructed from both macroscopic and microscopic perspectives, offering refined daily metrics to monitor and evaluate capacity utilization at regional, industry, and enterprise levels. this research addresses Shandong's industrial characteristics, facilitates the transition from old to new energy drivers, and monitors enterprise operational states. Case analyses of leading enterprises in Shandong's top industries and key transformation companies provide valuable data for economic assessment, transformation evaluation, production resumption monitoring, and risk anticipation. © 2024 ieee.

关键词： Energy utilization

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OT24:Optical color image encryption based on Two-dimensional quantum walks and 24-bit-plane permutation 10

OT24:Optical color image encryption based on Two-dimensional...

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10th ieee conference on big data security on cloud, bigdatasecurity 2024

作者： Cui, Guohao Wang, Hao Hao, Wentao Zhou, Xiaoyi Hainan University School of Cyberspace Security Haikou China

ISBN: (纸本)9798350389524

the Double Random Phase Encoding(DRPE) image encryption scheme has received a lot of attention when processing color images because it can encrypt R, G, and B components in parallel, in the era of big data. However, the DRPE scheme has two issues to be addressed: First, the DRPE scheme for color images ignores the correlation among R, G and B components, which leads to the decline of the robustness of the encrypted images: Second, DRPE schemes based on discrete Fourier transform (DFT) and discrete fractional Fourier transform (DFRFT) are vulnerable to linear attacks and known plain-text attacks (KPA). therefore, an optical color image encryption method based on two-dimensional quantum walking and 24-bit plane permutation is proposed in this paper. the phase modulation of DRPE utilizes random numbers generated by two-dimensional quantum walks, generalized Arnold used to confuse the imaginary and real parts of an encrypted image. 24-bit planar permutation reduces the correlation between the three layers (R, G, and B), while generalized Arnold enhances DRPE resistance to linear attacks. the effectiveness and safety of the scheme are verified by experiments. © 2024 ieee.

关键词： Discrete Fourier transforms

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A Dance Emotion Recognition Method Based on Movement Analysis and Federated Learning in Edge-cloud Hybrid Environments 10

A Dance Emotion Recognition Method Based on Movement Analysi...

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10th ieee conference on big data security on cloud, bigdatasecurity 2024

作者： Yang, Xinyi Li, Kaipei Tian, Jie Jishou University School of Music and Dance Jishou China Jishou University School of Computer Science and Engineering Jishou China

ISBN: (纸本)9798350389524

With the development of dance diversity, substantial volumes of big data associated with dance movements have been produced, predominantly in the form of videos and images. Dance uses movements to convey emotions, which is considered the essence of choreographic works. Movements used to express an inner emotional manifestation are the external factors that constitute dance, visible, and tangible actions. However, for online education, identifying dance emotions from the dance movements to support the grading of students' dance has become an urgent challenge. In this paper, we first construct a cloud-edge hybrid environment to be used for dance learning. then, we propose a novel approach for expressing dance emotions by utilizing advanced artificial intelligence techniques to encode different dance movements and emotions. Leveraging Laban Movement Analysis theory, we employ a federated learning framework in the hybrid environment to train dancer movements and guarantee the privacy of dancers. this process yields feature parameters related to the dancer's body structure, spatial orientation, and force dynamics. the original dance motion data is transformed into these three feature representation parameters, resulting in dance emotion data. the experimental results demonstrate that recognizing dance emotions from the perspective of dance movements is feasible, the accuracy increases by 4.22% than other state-of-the-art methods. © 2024 ieee.

关键词： big data

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Test Case Recommendation for Blockchain Systems Based on Graph Convolutional Networks 10

Test Case Recommendation for Blockchain Systems Based on Gra...

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10th ieee conference on big data security on cloud, bigdatasecurity 2024

作者： Li, Shuyi Zhang, Pengyu Liu, Xueyang Hu, Wenhui Peking University School of Software & Microelectronics Beijing China Big data center State Grid Corporation of China Beijing China Peking University National Engineering Research Center for Software Engineering Beijing China

ISBN: (纸本)9798350389524

In the development and operation of blockchain systems, the evaluation of these systems typically necessitates the formulation and execution of test cases. Current testing practices for blockchain systems often involve the manual specification of relevant test scenarios, with testing being conducted through inquiries or the execution of tasks within the system. However, due to the relatively narrow scope of real-world applications for blockchain systems, there exists a deficiency in appropriate test case sets for the evaluation of such systems. Consequently, this paper presents a Blockchain Test Case Recommendation System based on LightGCN, a simplified Graph Convolutional Neural Network. LightGCN dispenses with traditional graph convolutional network components such as non-linear activation and feature transformations, thereby enhancing the efficiency of model training. Experiments illustrate that this system effectively supplements and enhances the blockchain system's test case set by aggregating information from domain nodes. © 2024 ieee.

关键词： Blockchain

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Precision Assistance for Residents with Subsistence Allowance: Cluster Analysis of Mixed-Type data Based on K-Prototypes 10

Precision Assistance for Residents with Subsistence Allowanc...

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10th ieee conference on big data security on cloud, bigdatasecurity 2024

作者： Jing, Fangming Yang, Chen Li, Shuyi Liu, Xueyang Hu, Wenhui Information Center The Ministry of Civil Affairs of the People's Republic of China Beijing China Peking University Beijing China

ISBN: (纸本)9798350389524

this paper presents an empirical investigation into the refinement of precision assistance methodologies designed to benefit individuals receiving subsistence allowances. the central objective is to leverage advanced cluster analysis methodologies, particularly the k-prototypes algorithm, to scrutinize heterogeneous datasets, amalgamating both categorical and numerical variables. the overarching goal is to discern distinctive subpopulations within the beneficiary cohort, thereby facilitating the customization of assistance initiatives with greater efficacy. the methodological framework encompasses several key stages, including meticulous data preprocessing, the application of the k-prototypes algorithm for cluster formation, and precision assistance suggestions for clustering results. the findings of this inquiry underscore the efficacy of the proposed approach in delineating population segments accurately, thus offering promising prospects for optimizing resource allocation and refining service provision. In sum, this scholarly endeavor represents a significant stride forward in the realm of precision assistance strategies tailored for marginalized communities, thereby augmenting the operational efficiency and societal impact of extant social welfare endeavors. © 2024 ieee.

关键词： Cluster analysis

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