基于机器学习的匿名大数据访问控制系统设计

Design of Anonymous Big Data Access Control System Based on Machine Learning

为提高大数据环境下访问控制效率与数据匿名性,保障用户隐私和数据安全,本研究设计了一种基于机器学习技术的新型匿名大数据访问控制系统。通过对当前大数据访问控制技术、机器学习在访问控制中应用及匿名技术的发展与应用深入分析,采用B/S架构匿名大数据访问控制系统框架,系统软件模块主要由数据预处理、机器学习模型训练与评估、访问控制决策以及用户界面模块构成,为验证系统功能有效性进行了详细的对比实验。结果表明,该系统能提高大数据访问控制精确性与处理速度,有效保护数据的匿名性,本研究成果证明了采用机器学习技术优化大数据访问控制系统可行性与有效性。

In order to improve the efficiency of access control and data anonymity in big data environment,and ensure user privacy and data security,this study designed a new anonymous big data access control system based on machine learning technology.Through in-depth analysis of the currcnt big data access control technology,the application of machine learning in access control and the development and application of anonymous technology,the anonymous big data access control system framework of B/S architecture is adopted.The system software module is mainly composed of data preprocessing,machine learning model training and evaluation,access control decision making and user interface module.In order to verify the effectiveness of the system,a detailed comparative cxpcrimcnt is carricd out.The results show that this system can improve the accuracy and processing speed of big data access control,and effectively protect the anonymity of data.This research result proves the feasibility and effectiveness of using machine learning technology to optimize big data access control system.