Security attributes are the premise and foundation for implementing Attribute-Based Access Control(ABAC)mechanisms.However,when dealing with massive volumes of unstructured text big data resources,the current attribut...Security attributes are the premise and foundation for implementing Attribute-Based Access Control(ABAC)mechanisms.However,when dealing with massive volumes of unstructured text big data resources,the current attribute management methods based on manual extraction face several issues,such as high costs for attribute extraction,long processing times,unstable accuracy,and poor scalability.To address these problems,this paper proposes an attribute mining technology for access control institutions based on hybrid capsule networks.This technology leverages transfer learning ideas,utilizing Bidirectional Encoder Representations from Transformers(BERT)pre-trained language models to achieve vectorization of unstructured text data resources.Furthermore,we have designed a novel end-to-end parallel hybrid network structure,where the parallel networks handle global and local information features of the text that they excel at,respectively.By employing techniques such as attention mechanisms,capsule networks,and dynamic routing,effective mining of security attributes for access control resources has been achieved.Finally,we evaluated the performance level of the proposed attribute mining method for access control institutions through experiments on the medical referral text resource dataset.The experimental results show that,compared with baseline algorithms,our method adopts a parallel network structure that can better balance global and local feature information,resulting in improved overall performance.Specifically,it achieves a comprehensive performance enhancement of 2.06%to 8.18%in the F1 score metric.Therefore,this technology can effectively provide attribute support for access control of unstructured text big data resources.展开更多
目前空间数据网格中的访问权限控制基本是采用的设置用户角色和空间数据分级映射的机制,不能满足开放网格环境中的访问主体属性多样化和访问数据细粒度控制的要求。针对该问题,研究并提出了一种基于属性的空间数据安全访问机制。提出了...目前空间数据网格中的访问权限控制基本是采用的设置用户角色和空间数据分级映射的机制,不能满足开放网格环境中的访问主体属性多样化和访问数据细粒度控制的要求。针对该问题,研究并提出了一种基于属性的空间数据安全访问机制。提出了基于ABAC(attribute based access control)的目标用户、空间数据、网格环境的属性描述模型;在XACML(extensible access control markup language)的基础上,设计了完整的基于属性的空间数据访问策略、框架和流程。通过该项目的应用,表明了该机制能够有效满足空间数据网格访问控制要求。展开更多
基金supported by National Natural Science Foundation of China(No.62102449).
文摘Security attributes are the premise and foundation for implementing Attribute-Based Access Control(ABAC)mechanisms.However,when dealing with massive volumes of unstructured text big data resources,the current attribute management methods based on manual extraction face several issues,such as high costs for attribute extraction,long processing times,unstable accuracy,and poor scalability.To address these problems,this paper proposes an attribute mining technology for access control institutions based on hybrid capsule networks.This technology leverages transfer learning ideas,utilizing Bidirectional Encoder Representations from Transformers(BERT)pre-trained language models to achieve vectorization of unstructured text data resources.Furthermore,we have designed a novel end-to-end parallel hybrid network structure,where the parallel networks handle global and local information features of the text that they excel at,respectively.By employing techniques such as attention mechanisms,capsule networks,and dynamic routing,effective mining of security attributes for access control resources has been achieved.Finally,we evaluated the performance level of the proposed attribute mining method for access control institutions through experiments on the medical referral text resource dataset.The experimental results show that,compared with baseline algorithms,our method adopts a parallel network structure that can better balance global and local feature information,resulting in improved overall performance.Specifically,it achieves a comprehensive performance enhancement of 2.06%to 8.18%in the F1 score metric.Therefore,this technology can effectively provide attribute support for access control of unstructured text big data resources.
文摘目前空间数据网格中的访问权限控制基本是采用的设置用户角色和空间数据分级映射的机制,不能满足开放网格环境中的访问主体属性多样化和访问数据细粒度控制的要求。针对该问题,研究并提出了一种基于属性的空间数据安全访问机制。提出了基于ABAC(attribute based access control)的目标用户、空间数据、网格环境的属性描述模型;在XACML(extensible access control markup language)的基础上,设计了完整的基于属性的空间数据访问策略、框架和流程。通过该项目的应用,表明了该机制能够有效满足空间数据网格访问控制要求。
