Application of Big Data Privacy Protection Based on Edge Computing in the Prediction of Martial Arts Training Movement Trajectory

IF 0.6 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Xing Li, ZhiYing Cui, FeiFei Zhang, Li Li
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引用次数: 0

Abstract

With the development of science and technology, edge computing and big data privacy protection are more and more widely used in various fields. The application of big data privacy protection based on edge computing in the prediction of sport trajectories for martial arts training shows good performance and privacy protection. Edge computing can process and analyze data in real time to improve the accuracy and efficiency of sport trajectory prediction. Big data privacy protection can ensure the security of athletes' personal information and training data and prevent data leakage and misuse. However, existing related works still have some deficiencies in data processing speed, accuracy, and privacy protection. In this paper, the authors address these issues and propose an edge computing-based big data privacy protection method to improve the accuracy and security of sport trajectory prediction for martial arts training.
基于边缘计算的大数据隐私保护在武术训练运动轨迹预测中的应用
随着科学技术的发展,边缘计算和大数据隐私保护在各个领域的应用越来越广泛。基于边缘计算的大数据隐私保护在武术训练运动轨迹预测中的应用显示出良好的性能和隐私保护效果。边缘计算可以实时处理和分析数据,提高运动轨迹预测的准确性和效率。大数据隐私保护可以确保运动员个人信息和训练数据的安全,防止数据泄露和滥用。然而,现有的相关工作在数据处理速度、准确性和隐私保护方面仍存在一些不足。本文作者针对这些问题,提出了一种基于边缘计算的大数据隐私保护方法,以提高武术训练中运动轨迹预测的准确性和安全性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
2.60
自引率
0.00%
发文量
24
期刊介绍: Organizations are continuously overwhelmed by a variety of new information technologies, many are Web based. These new technologies are capitalizing on the widespread use of network and communication technologies for seamless integration of various issues in information and knowledge sharing within and among organizations. This emphasis on integrated approaches is unique to this journal and dictates cross platform and multidisciplinary strategy to research and practice.
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