Mobile SNS popular topics of mobile is one of the most Internet. In order to fulfill the user demand for self-maintained independent social network and ensure the privacy of their personal information and resources, t...Mobile SNS popular topics of mobile is one of the most Internet. In order to fulfill the user demand for self-maintained independent social network and ensure the privacy of their personal information and resources, the paper proposes system architecture of decentralized mobile SNS.In the temporary scenarios, the system makes use of the existent specification of FOAF （Friend- of-a-Friend） to describe users＇ personal information and act as a certificate to be identified by SNS sites. Ticket-based Access Authorization System （TAAS） is provided to grant permission to acquire resources on personal portal. Meanwhile, the mechanism and algorithm are devised for user profile complete deletion when users are going to quit the service for the temporary scenarios.展开更多
The National Institute of Standards and Technology(NIST)has identified natural language policies as the preferred expression of policy and implicitly called for an automated translation of ABAC natural language access...The National Institute of Standards and Technology(NIST)has identified natural language policies as the preferred expression of policy and implicitly called for an automated translation of ABAC natural language access control policy(NLACP)to a machine-readable form.To study the automation process,we consider the hierarchical ABAC model as our reference model since it better reflects the requirements of real-world organizations.Therefore,this paper focuses on the questions of:how can we automatically infer the hierarchical structure of an ABAC model given NLACPs;and,how can we extract and define the set of authorization attributes based on the resulting structure.To address these questions,we propose an approach built upon recent advancements in natural language processing and machine learning techniques.For such a solution,the lack of appropriate data often poses a bottleneck.Therefore,we decouple the primary contributions of this work into:(1)developing a practical framework to extract authorization attributes of hierarchical ABAC system from natural language artifacts,and(2)generating a set of realistic synthetic natural language access control policies(NLACPs)to evaluate the proposed framework.Our experimental results are promising as we achieved-in average-an F1-score of 0.96 when extracting attributes values of subjects,and 0.91 when extracting the values of objects’attributes from natural language access control policies.展开更多
The National Institute of Standards and Technology(NIST)has identified natural language policies as the preferred expression of policy and implicitly called for an automated translation of ABAC natural language access...The National Institute of Standards and Technology(NIST)has identified natural language policies as the preferred expression of policy and implicitly called for an automated translation of ABAC natural language access control policy(NLACP)to a machine-readable form.To study the automation process,we consider the hierarchical ABAC model as our reference model since it better reflects the requirements of real-world organizations.Therefore,this paper focuses on the questions of:how can we automatically infer the hierarchical structure of an ABAC model given NLACPs;and,how can we extract and define the set of authorization attributes based on the resulting structure.To address these questions,we propose an approach built upon recent advancements in natural language processing and machine learning techniques.For such a solution,the lack of appropriate data often poses a bottleneck.Therefore,we decouple the primary contributions of this work into:(1)developing a practical framework to extract authorization attributes of hierarchical ABAC system from natural language artifacts,and(2)generating a set of realistic synthetic natural language access control policies(NLACPs)to evaluate the proposed framework.Our experimental results are promising as we achieved-in average-an F1-score of 0.96 when extracting attributes values of subjects,and 0.91 when extracting the values of objects’attributes from natural language access control policies.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.61272521the Specialized Research Fund for the Doctoral Program of Higher Education under Grant No.20110005130001
文摘Mobile SNS popular topics of mobile is one of the most Internet. In order to fulfill the user demand for self-maintained independent social network and ensure the privacy of their personal information and resources, the paper proposes system architecture of decentralized mobile SNS.In the temporary scenarios, the system makes use of the existent specification of FOAF （Friend- of-a-Friend） to describe users＇ personal information and act as a certificate to be identified by SNS sites. Ticket-based Access Authorization System （TAAS） is provided to grant permission to acquire resources on personal portal. Meanwhile, the mechanism and algorithm are devised for user profile complete deletion when users are going to quit the service for the temporary scenarios.
文摘The National Institute of Standards and Technology(NIST)has identified natural language policies as the preferred expression of policy and implicitly called for an automated translation of ABAC natural language access control policy(NLACP)to a machine-readable form.To study the automation process,we consider the hierarchical ABAC model as our reference model since it better reflects the requirements of real-world organizations.Therefore,this paper focuses on the questions of:how can we automatically infer the hierarchical structure of an ABAC model given NLACPs;and,how can we extract and define the set of authorization attributes based on the resulting structure.To address these questions,we propose an approach built upon recent advancements in natural language processing and machine learning techniques.For such a solution,the lack of appropriate data often poses a bottleneck.Therefore,we decouple the primary contributions of this work into:(1)developing a practical framework to extract authorization attributes of hierarchical ABAC system from natural language artifacts,and(2)generating a set of realistic synthetic natural language access control policies(NLACPs)to evaluate the proposed framework.Our experimental results are promising as we achieved-in average-an F1-score of 0.96 when extracting attributes values of subjects,and 0.91 when extracting the values of objects’attributes from natural language access control policies.
基金supported by Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The National Institute of Standards and Technology(NIST)has identified natural language policies as the preferred expression of policy and implicitly called for an automated translation of ABAC natural language access control policy(NLACP)to a machine-readable form.To study the automation process,we consider the hierarchical ABAC model as our reference model since it better reflects the requirements of real-world organizations.Therefore,this paper focuses on the questions of:how can we automatically infer the hierarchical structure of an ABAC model given NLACPs;and,how can we extract and define the set of authorization attributes based on the resulting structure.To address these questions,we propose an approach built upon recent advancements in natural language processing and machine learning techniques.For such a solution,the lack of appropriate data often poses a bottleneck.Therefore,we decouple the primary contributions of this work into:(1)developing a practical framework to extract authorization attributes of hierarchical ABAC system from natural language artifacts,and(2)generating a set of realistic synthetic natural language access control policies(NLACPs)to evaluate the proposed framework.Our experimental results are promising as we achieved-in average-an F1-score of 0.96 when extracting attributes values of subjects,and 0.91 when extracting the values of objects’attributes from natural language access control policies.
