An Improved RBAC Model and its Application

Zhang, Zi; Huang, Ting-lei

doi:10.1109/wgec.2009.203

Cited by 3 publications

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The combination of ontology and access control improves the extensibility of the model and has a better performance. Literature [19] proposes a dynamic role access control model combining attribute values. In this model, the control of dynamic roles is realized by associating attributes and roles, and the inheritable method is introduced in the data, so that the original static roles become dynamic roles, and the authorization operation of roles is simplified, which greatly improves the extensibility of the model.…”

Section: Related Workmentioning

confidence: 99%

A Novel Framework for Authority Management Based on Knowledge Base Completion of the Graph Neural Network

Wang

Xia

Zhao

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Big data is massive and heterogeneous, along with the rapid increase in data quantity, and the diversification of user access, traditional database, and access control methods can no longer meet the requirements of big data storage and flexible access control. To solve this problem, an entity relationship completion and authority management method is proposed. By combining the weighted graph convolutional neural network and the attention mechanism, a knowledge base completion model is given. On this basis, the authority management model is formally defined and the process of multilevel trust access control is designed. The effectiveness of the proposed method is verified by experiments, and the authority management of knowledge base is more fine-grained and more secure.

show abstract

Section: Related Workmentioning

confidence: 99%

A Novel Framework for Authority Management Based on Knowledge Base Completion of the Graph Neural Network

Wang

Xia

Zhao

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…The K3 is constructed from a number of other networks, with at least three layers formed by K2-sets, and has been used successfully in face detection [5] [8], robot navigation [9], recognition of spoken digits [10], [11], classification of structures in tissues [12], teas [13] and text [14], prediction of time series [15], motor imagery [16], and clustering [17]. It has also been used successfully for EEG classification [18]- [22]. The equation for the K3-set is described by…”

Section: K3-setmentioning

confidence: 99%

A simulator for Freeman K-sets in Java

Piazentin

Rosa

2015

2015 International Joint Conference on Neural Networks (IJCNN)

View full text Add to dashboard Cite

Abstract-Freeman K-sets form a hierarchy of models of the dynamics of neuron populations at the mesoscopic (intermediate) level. The topology of connections is modeled by networks of excitatory and inhibitory populations of neurons; the dynamics is approximated by piecewise Iinearization of nonlinear ordinary differential equations (ODE). A simulator for the K-sets of levels Ko to K3 was built in Java in order to remedy some of the deficiencies found in the simulator available built in MATLAB, as defects that cause instabilities in the simulation, the lack of flexibility in the parameterization of the K3-set and the high computational time required to use the simulator due to design and language decisions. Java was chosen because it is a popular, multi-platform, object oriented and computationally efficient language. The software developed in Java also allows easy integration with MATLAB and other environments. An application on clustering of social networking data evaluates the proposed simulator.

show abstract