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检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

共 6946 条记录，以下是551-560 订阅

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Secure Encryption Scheme for Medical data based on Homomorphic Encryption

Secure Encryption Scheme for Medical Data based on Homomorph...

引用

2023 international conference on data Science and Network Security, ICDSNS 2023

作者： Xu, Liaoran Zhao, Chenyang Jiang, Weili Ye, Jun Zhao, Yan Zhang, Zhengqi School of Cyberspace Security Hainan University Key Laboratory of Internet Information Retrieval of Hainan Province Haikou China Hainan Provincial Public Security Department Haikou China Shanwei Institute of Techmology Shanwei China Key Laboratory of Internet Information Retrieval of Hainan Province Haikou China

ISBN: (纸本)9798350301595

With the development of cloud computing, cloud healthcare has brought convenience to users and improved the utilization of healthcare resources, but it also brings some security risks. To this end, this paper proposes a new solution that combines homomorphic encryption technology and domestic cryptographic algorithms for protecting the privacy of cloud medical electronic medical records. It is found that by localizing user keys, data leakage due to malicious damage can be effectively prevented. In addition, this study proposes a hybrid encryption scheme and a ciphertext digest-based search and statistics approach to reduce the computational overhead required to decrypt all electronic medical records. The scheme is the first to use a combination of domestic cryptographic algorithms and homomorphic encryption techniques in a cloud medical data storage and computing application scenario to achieve secure and reliable cloud medical data storage and computing. In addition, the study provides several recommendations for healthcare organizations and cloud platform service providers to help them better protect the security and privacy of healthcare data. © 2023 IEEE.

关键词： Cryptography

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Third international Workshop on Big data in Emergent Distributed Environments (BiDEDE) 23

Third International Workshop on Big Data in Emergent Distrib...

引用

2023 ACM/SIGMOD international conference on Management of data, SIGMOD 2023

作者： Groppe, Sven Gruenwald, Le Hsu, Ching-Hsien University of Lübeck Lübeck Germany University of Oklahoma Norman Oklahoma United States College of Information and Electrical Engineering Asia University Taichung Taiwan

ISBN: (纸本)9781450395076

The Third international Workshop on Big data in Emergent Distributed Environments (BiDEDE) is centered around addressing scalable data management issues in emerging computing environments such as (post) cloud and fog/edge/dew computing. These environments aim to incorporate efficient data management and processing into distributed systems to minimize communication and computational costs, while simultaneously enhancing application throughput, reducing latencies, and prolonging battery life for nodes. Despite over a decade of research in this area, there are still numerous challenges that remain unsolved due to technological advancements such as lightweight virtualization, greater node capabilities, and increased parallelization. This year we also welcome contributions on data management for or solved by quantum computing. The workshop provides a platform for active discussions in these and related topics. © 2023 Owner/Author.

关键词： Information management

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Application of data Fusion algorithms in the data processing of Intelligent Greenhouses 1

引用

13th international conference on Frontier computing, FC 2023

作者： Li, Jinguang Li, Dan College of Information and Control Engineering Jilin Institute of Chemical Technology Jilin Jilin City China Electrical and Information Engineering College Jilin Agricultural Science and Technology University Jilin Jilin City China

ISBN: (数字)9789819992997

ISBN: (纸本)9789819992980

Greenhouses can ensure the normal growth of crops in extreme environments. With the popularization of social intelligence, how to build an efficient and accurate environmental monitoring system to ensure that crops have suitable growth conditions is a top priority. In order to effectively solve the problem of data missing and large data errors caused by sensor collection in intelligent greenhouses under extreme environments, this paper uses data preprocessing and multi-sensor fusion algorithms to ensure the accuracy of data. The effectiveness of the data fusion algorithm is verified through data transmission experiments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Crops

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Research on the Construction of a Knowledge Graph Information Search Engine in the web Domain

Research on the Construction of a Knowledge Graph Informatio...

引用

2023 international conference on Evolutionary algorithms and Soft computing Techniques, EASCT 2023

作者： Zhou, Yuzhong Lin, Zhengping Shi, Jiahao Yang, Yuliang Wei, Ronghui China Southern Power Grid Research Institute Guangzhou Guangdong China

ISBN: (纸本)9798350313413

The article proposes a construction approach for a knowledge graph data search engine in the field of power grids. A detailed analysis is conducted on the construction, design, and operational implementation of the knowledge graph. A search engine system based on fuzzy algorithms and a search engine system integrating domain feature knowledge graphs are designed, which can recognize entities/assertions based on the Text Rank model, accurately search for external knowledge based on semantic methods, and set a global KG update strategy. The algorithm can achieve high entity/assertion recognition accuracy, automatically map domain knowledge to local KG, quickly update the service knowledge base online, and achieve high accuracy answers with low response delay. According to the testing results of the information and communication branch of the power company. The accuracy and recall of the keyword search algorithm in this method are 6.62%, 7.96%, and 2.24% higher, respectively. It can effectively overcome the ambiguity of power grid problem expression and accurately search for power grid knowledge. © 2023 IEEE.

关键词： domain data knowledge graph power grid search

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Application of Automatic Collection of Real-time data Based on Clustering Algorithm in Training Assessment

Application of Automatic Collection of Real-time Data Based ...

引用

2023 international conference on Networking, Informatics and computing, ICNETIC 2023

作者： Xie, Zhongshi Xu, Qingyao Wang, Yaru Space Engineering University Beijing100011 China

ISBN: (纸本)9798350313314

For the sports field, the training process and teaching are both complicated sports processes. According to the characteristics and processes of sports training, the movements in sports training can be shown to students and sports teachers again, thus playing a role in sports training. In this paper, a sports training data assessment model based on clustering algorithm is put forward, which combines the data management equation and the response time of data collection to realize the automatic collection of real-time sports training data. Through the simulation application effect of the model, its rationality is tested, thus improving the existing decision support system. Applying fuzzy clustering analysis to the analysis of sports training assessment data can reflect the diversity of group thinking more objectively. By improving the clustering algorithm, the efficiency of clustering analysis is improved. © 2023 IEEE.

关键词： Clustering algorithms

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Time-dependent Pricing and Scheduling for Cloud Object Storage Service Providers 16

Time-dependent Pricing and Scheduling for Cloud Object Stora...

引用

IEEE 16th international conference on Cloud computing (IEEE CLOUD)

作者： Lee, Kyungtae Kim, Yeongjin Inha Univ Dept Elect Engn Incheon South Korea

ISBN: (纸本)9798350304817

There is increasing competition among cloud object storage service (COSS) providers as demand for COSSs grows. While various pricing models exist for commercial COSS providers, they do not effectively adapt to changing client demand and resource supply. As a result, many COSS providers are still facing fundamental problems in operational strategy to maximize their profit. In this paper, we propose TD-PnS based on the Lyapunov-drift-minus-profit technique which jointly and dynamically makes decisions on (i) service pricing, (ii) CPU clock scaling and encoding scheduling, (iii) network scheduling, and (iv) energy storage management to maximize COSS provider's profits. We also propose an additional version of TD-PnS, namely TD-PnS-Adv, that adds realistic aspects such as system stabilization. Finally, through trace-driven simulation using real dataset, we demonstrate that the proposed algorithms outperform existing algorithms and pricing models in terms of profit.

关键词： Object storage, time dependent pricing, service provider, energy storage system, data encoding

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Swarm Intelligence algorithms for Profit Maximization and Delay Minimization in Internet data Centers 10

Swarm Intelligence Algorithms for Profit Maximization and De...

引用

10th international conference on Signal processing and Integrated Networks, SPIN 2023

作者： Paul, Haritha Mini, S. National Institute of Technology Goa Department of Computer Science and Engineering Goa 403401 India

ISBN: (纸本)9781665490993

Internet data Centers have increasingly gained importance in recent days. organizations require their data centers to run with minimum expenditure to improve profits, and the service requests need to be fulfilled with the minimum service delay possible. Increasing profits and minimizing service delay are challenges faced by data centers on a day-to-day basis. The demand response in a data center is a crucial challenge to be optimized because of its direct correlation with profits and delay in an internet data center. In this paper, meta-heuristic swarm algorithms are analyzed to find the best possible profits and minimum possible delays that can arise in a data center for a particular number of active servers and the amount of workload processed by the data center. The methods are verified with real-world data for pricing schemes of electricity, power consumption, request loads and data center specifications. © 2023 IEEE.

关键词： Profitability

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Towards Detecting Tonic Information processing Activities with Physiological data

Towards Detecting Tonic Information Processing Activities wi...

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ACM international Joint conference on Pervasive and Ubiquitous computing / ACM international Symposium on Wearable computing (UbiComp/ISWC)

作者： Ji, Kaixin Spina, Damiano Hettiachchi, Danula Salim, Flora D. Scholer, Falk RMIT Univ Melbourne Australia Univ New South Wales Sydney Australia

ISBN: (纸本)9798400702006

Characterizing Information processing Activities (IPAs) such as reading, listening, speaking, and writing, with physiological signals captured by wearable sensors can broaden the understanding of how people produce and consume information. However, sensors are highly sensitive to external conditions that are not trivial to control - not even in lab user studies. We conducted a pilot study (N = 7) to assess the robustness and sensitivity of physiological signals across four IPAs (READ, LISTEN, SPEAK, and WRITE) using multiple sensors. The collected signals include Electrodermal Activities, Blood Volume Pulse, gaze, and head motion. We observed consistent trends across participants, and ten features with statistically significant differences across the four IPAs. Our results provide preliminary quantitative evidence of differences in physiological responses when users encounter IPAs, revealing the necessity to inspect the signals separately according to the IPAs. The next step of this study moves into a specific context, information retrieval, and the IPAs are considered as the interaction modalities with the search system, for instance, submitting the search query by speaking or typing.

关键词： information processing activities physiological signals information seeking

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CryptodataMR: Enhancing the data Protection Using Cryptographic Hash and Encryption/Decryption Through MapReduce Programming Model 1

引用

6th international conference on Innovative computing and Communication, ICICC 2023

作者： Brindha, G. Siva Gobi, M. Tamil Nadu Coimbatore India

ISBN: (数字)9789819933150

ISBN: (纸本)9789819933143

Cloud computing has become the most significant technology in today’s world as it provides several services including physical resources and platforms for their users through distributed and parallel computing. Due to the usage of technology and its facilities, large data has been stored and retrieved by their users. Thus, processing a huge volume of data has become progressively essential to satisfy their business and other private users effectively. A tool that is most popularly used for processing this big data is MapReduce. However, providing security for the private data stored by the users is the most challenging task. Despite using several security measures such as authentication, providing complete security for private data is almost critical as there is a possibility of compromising the security policies. Thus, there is an exigent need to protect data from leakage and other security breaches. This paper presents a solution CryptodataMR model to enhance data protection by implementing cryptographic hash and encryption/decryption algorithms in the MapReduce programming model. The proposed model uses a chain rolling double hash algorithm at the map layer and position-based shift hill cipher at the reduce layer for retaining data privacy. This model secures the data effectively through simple cryptographic algorithms with intricate results. Experimental analysis has been made for the proposed model to verify the performance in enhancing data security. The results show that the proposed model provides better performance with minimum execution time and ensures the privacy of the data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： data privacy

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Semi-asynchronous Federated Learning Optimized for NON-IID data Communication based on Tensor Decomposition 21

Semi-asynchronous Federated Learning Optimized for NON-IID D...

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21st IEEE international Symposium on Parallel and Distributed processing with Applications, 13th IEEE international conference on Big data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Wu, Qidong Fu, Xuemei Yang, Xiangli Zhao, Ruonan Wu, Chengqian Zhang, Tinghua Hainan University School of Computer Science and Technology Haikou China Hainan University School of Information and Communication Engineering Haikou China

ISBN: (纸本)9798350329223

Federated Learning (FL) is a new distributed machine learning framework that enables reliable collaborative training without collecting users' private data, it can successfully address the issue of data silos. However, FL has had trouble scaling to statistically varied data and large-scale models because of its frequent communication and average aggregation procedures. In this paper, we propose a Semi-asynchronous Stochastic Controlled Averaging based on Tensor Decomposition for Federated Learning (SATD-SCAFFOLD), in which we perform tensor decomposition on the client model to reduce communication costs. In addition, we design a semi-asynchronous aggregation approach for the server, which can prevent server from wasting too much time waiting for data from late clients in the case of an unstable network, and the overall model still has excellent performance. Thorough experiments demonstrate that our proposed SATDSCAFFOLD algorithm can reduce both the communication cost and convergence time while maintaining good performance. © 2023 IEEE.

关键词： Stochastic systems

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