Pengcheng Zhang, Yingtao Sun, Wenrui Li, Wei Song, H. Leung
Quality of Service (QoS) is considered as an important factor to determine the success of a Web Service. Currently, many QoS prediction approaches focus on time series models. However, these approaches only consider linear and nonlinear time series. Analysis of real QoS datasets shows that they are characterized by other behaviors. Incomplete characteristics analysis of existing prediction approaches will result in wrong prediction results. Furthermore, the collected QoS values may miss some data, which will also impact the prediction accuracy. RBF (Radial Basis Function) neural network model can manage the complex linear and nonlinear relationship, with great flexibility and adaptability. Therefore, we propose a novel combinational prediction approach for QoS based on RBF, which chooses the optimal model from the established linear or nonlinear prediction model, and dynamic gray prediction model according to the data characteristics. Next, the predicted results of these models are passed into the RBF training model as the input, and then used for prediction. Using a public QoS dataset and four real-world QoS datasets, we evaluate the proposed approach by comparing it with previous approach. The experimental results show that our approach is better and improves the accuracy and validity.
{"title":"A Combinational QoS-Prediction Approach Based on RBF Neural Network","authors":"Pengcheng Zhang, Yingtao Sun, Wenrui Li, Wei Song, H. Leung","doi":"10.1109/SCC.2016.81","DOIUrl":"https://doi.org/10.1109/SCC.2016.81","url":null,"abstract":"Quality of Service (QoS) is considered as an important factor to determine the success of a Web Service. Currently, many QoS prediction approaches focus on time series models. However, these approaches only consider linear and nonlinear time series. Analysis of real QoS datasets shows that they are characterized by other behaviors. Incomplete characteristics analysis of existing prediction approaches will result in wrong prediction results. Furthermore, the collected QoS values may miss some data, which will also impact the prediction accuracy. RBF (Radial Basis Function) neural network model can manage the complex linear and nonlinear relationship, with great flexibility and adaptability. Therefore, we propose a novel combinational prediction approach for QoS based on RBF, which chooses the optimal model from the established linear or nonlinear prediction model, and dynamic gray prediction model according to the data characteristics. Next, the predicted results of these models are passed into the RBF training model as the input, and then used for prediction. Using a public QoS dataset and four real-world QoS datasets, we evaluate the proposed approach by comparing it with previous approach. The experimental results show that our approach is better and improves the accuracy and validity.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121365427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Software-defined networks (SDNs) have been recognized as the next-generation networking paradigm that decouples the data forwarding from the centralized control. To realize the merits of dedicated QoS provisioning and fast route (re-)configuration services over the decoupled SDNs, various QoS requirements in packet delay, loss, and throughput should be supported by an efficient transportation with respect to each specific application. In this paper, a QoS-aware adaptive routing (QAR) is proposed in the designed multi-layer hierarchical SDNs. Specifically, the distributed hierarchical control plane architecture is employed to minimize signaling delay in large SDNs via three-levels design of controllers, i.e., the super, domain (or master), and slave controllers. Furthermore, QAR algorithm is proposed with the aid of reinforcement learning and QoS-aware reward function, achieving a time-efficient, adaptive, QoS-provisioning packet forwarding. Simulation results confirm that QAR outperforms the existing learning solution and provides fast convergence with QoS provisioning, facilitating the practical implementations in large-scale software service-defined networks.
{"title":"QoS-Aware Adaptive Routing in Multi-layer Hierarchical Software Defined Networks: A Reinforcement Learning Approach","authors":"Shih-Chun Lin, I. Akyildiz, Pu Wang, Min Luo","doi":"10.1109/SCC.2016.12","DOIUrl":"https://doi.org/10.1109/SCC.2016.12","url":null,"abstract":"Software-defined networks (SDNs) have been recognized as the next-generation networking paradigm that decouples the data forwarding from the centralized control. To realize the merits of dedicated QoS provisioning and fast route (re-)configuration services over the decoupled SDNs, various QoS requirements in packet delay, loss, and throughput should be supported by an efficient transportation with respect to each specific application. In this paper, a QoS-aware adaptive routing (QAR) is proposed in the designed multi-layer hierarchical SDNs. Specifically, the distributed hierarchical control plane architecture is employed to minimize signaling delay in large SDNs via three-levels design of controllers, i.e., the super, domain (or master), and slave controllers. Furthermore, QAR algorithm is proposed with the aid of reinforcement learning and QoS-aware reward function, achieving a time-efficient, adaptive, QoS-provisioning packet forwarding. Simulation results confirm that QAR outperforms the existing learning solution and provides fast convergence with QoS provisioning, facilitating the practical implementations in large-scale software service-defined networks.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128662925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Television broadcast production facilities capture, manage, edit, handle, and broadcast audiovisual content by using a wide array of specialized equipment and software. The complex workflow in this environment demands interoperability between devices, but vendor-neutral protocols do not provide access to a significant amount of functionality. This paper proposes the adoption of a Service-Oriented Architecture for controlling broadcasting equipment, addressing difficulties specific to this environment such as the prevalence of non-Web Services, embedded devices, and constrained computational resources. The proposed solution is centered on a semantic service registry, which is able to compose mediators and produces stubs in response to service selection requests. The prototype registry is experimentally evaluated in simulated scenarios, focusing on how size and complexity of the broadcast facility impact on response times.
{"title":"A Service-Oriented Approach for Integrating Broadcast Facilities","authors":"Alexis Huf, I. Salvadori, Frank Siqueira","doi":"10.1109/SCC.2016.97","DOIUrl":"https://doi.org/10.1109/SCC.2016.97","url":null,"abstract":"Television broadcast production facilities capture, manage, edit, handle, and broadcast audiovisual content by using a wide array of specialized equipment and software. The complex workflow in this environment demands interoperability between devices, but vendor-neutral protocols do not provide access to a significant amount of functionality. This paper proposes the adoption of a Service-Oriented Architecture for controlling broadcasting equipment, addressing difficulties specific to this environment such as the prevalence of non-Web Services, embedded devices, and constrained computational resources. The proposed solution is centered on a semantic service registry, which is able to compose mediators and produces stubs in response to service selection requests. The prototype registry is experimentally evaluated in simulated scenarios, focusing on how size and complexity of the broadcast facility impact on response times.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115867381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Predicting inventory shipments can be useful for lean inventory management such as inventory planning. In this paper, we propose approaches to predict inventory shipments based on the data extracted from the inventory management module of Oracle EBS systems of a GPS-manufacturing company. First, we introduce the process to extract the inventory shipment data from the Oracle EBS system. Then, we adopt time series forecasting algorithms (i.e., ARMA) and Primitive KNN algorithms to predict the future shipments for a group of inventory items. At last, by discovering the patterns of parameter settings when optimal prediction accuracy is achieved, we develop new algorithms to reduce runtime at different levels with trade-offs in prediction accuracy.
{"title":"Predicting Inventory Shipments of Oracle EBS Systems","authors":"Yuqun Zhang, D. Perry","doi":"10.1109/SCC.2016.73","DOIUrl":"https://doi.org/10.1109/SCC.2016.73","url":null,"abstract":"Predicting inventory shipments can be useful for lean inventory management such as inventory planning. In this paper, we propose approaches to predict inventory shipments based on the data extracted from the inventory management module of Oracle EBS systems of a GPS-manufacturing company. First, we introduce the process to extract the inventory shipment data from the Oracle EBS system. Then, we adopt time series forecasting algorithms (i.e., ARMA) and Primitive KNN algorithms to predict the future shipments for a group of inventory items. At last, by discovering the patterns of parameter settings when optimal prediction accuracy is achieved, we develop new algorithms to reduce runtime at different levels with trade-offs in prediction accuracy.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117319629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the rapid development of Internet technology, a new architecture named Content-Centric Networking (CCN) has emerged recently. With powerful abilities of data caching and multicast, it has been increasingly popular especially for multimedia applications. However, service markup, in-network caching and routing are not inherently addressed in CCN. In this paper, we make an attempt at filling this gap by designing an ontology-based semantic service markup for CCN. Two components which are Service Identifier (SID) and Service Behavior Description (SBD) are presented in order to decouple the service entity with its original physical location, making it possible for service discovery and caching in CCN environments. Detailed designs are introduced, and a case study of an online multimedia conference system is presented to validate the effectiveness of our approach.
{"title":"An Ontology-Based Semantic Service Markup for Content-centric Networking","authors":"Yuze Huang, Jiwei Huang, Budan Wu, Tianxiang Yao, Shuqing He, Junliang Chen","doi":"10.1109/SCC.2016.109","DOIUrl":"https://doi.org/10.1109/SCC.2016.109","url":null,"abstract":"With the rapid development of Internet technology, a new architecture named Content-Centric Networking (CCN) has emerged recently. With powerful abilities of data caching and multicast, it has been increasingly popular especially for multimedia applications. However, service markup, in-network caching and routing are not inherently addressed in CCN. In this paper, we make an attempt at filling this gap by designing an ontology-based semantic service markup for CCN. Two components which are Service Identifier (SID) and Service Behavior Description (SBD) are presented in order to decouple the service entity with its original physical location, making it possible for service discovery and caching in CCN environments. Detailed designs are introduced, and a case study of an online multimedia conference system is presented to validate the effectiveness of our approach.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114843396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As more and more software systems are used in our day-to-day life from traveling, shopping to consuming health and financial services, our lives and habits ar e becoming highly dependent on the trustworthiness of these systems. It is important for such systems to operate continuously while satisfying users' functional, non-functional (such as performance, availability, and other QoS attributes), and trust (i.e., the degree of compliance of a system to its specification) requirements. However, it is a challenge to design such systems that are specially able to self-adapt to changes in its environments (which we refer as context) in real-time. In this paper, we propose a Bayesian network-based framework to help the development of such self-adaptive software in distributed system domain while ensuring a continuous satisfaction of the QoS and trust values of the systems. We have applied the proposed framework to a case study and the results show the effectiveness of the framework in designing QoS and trust based self-adaptive systems.
{"title":"A QoS and Trust Adaptation Framework for Composed Distributed Systems","authors":"Dimuthu Gamage, Lahiru S. Gallege, R. Raje","doi":"10.1109/SCC.2016.40","DOIUrl":"https://doi.org/10.1109/SCC.2016.40","url":null,"abstract":"As more and more software systems are used in our day-to-day life from traveling, shopping to consuming health and financial services, our lives and habits ar e becoming highly dependent on the trustworthiness of these systems. It is important for such systems to operate continuously while satisfying users' functional, non-functional (such as performance, availability, and other QoS attributes), and trust (i.e., the degree of compliance of a system to its specification) requirements. However, it is a challenge to design such systems that are specially able to self-adapt to changes in its environments (which we refer as context) in real-time. In this paper, we propose a Bayesian network-based framework to help the development of such self-adaptive software in distributed system domain while ensuring a continuous satisfaction of the QoS and trust values of the systems. We have applied the proposed framework to a case study and the results show the effectiveness of the framework in designing QoS and trust based self-adaptive systems.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"09 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127237026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
BPEL(Business Process Execution Language) composite service evolves a lot in its lifetime. Regression testing must be performed to ensure the correctness of each evolved version. In this article, an approach is proposed to select test cases for regression testing based on data flow testing criterion. With XCFG(eXtended Control Flow Graph) modeling BPEL composite service, the approach improves the traditional data flow analysis to compute the def-use pairs in BPEL process, and then identifies the affected def-use pairs by comparing the def-use pairs and XCFG model in the evolved version with those in the baseline version, where related WSDL(Web Service Description Language) documents are incorporated for comparison. The data flow paths covering the affected def-use pairs are calculated for regression testing, and some of them can reuse the test cases in the baseline version, which are determined by analyzing the path condition of data flow paths between two versions. The proposed approach can detect three kinds of changes, including process change, binding change and interface change. Experimental study shows the effectiveness.
{"title":"Test Case Selection for Data Flow Based Regression Testing of BPEL Composite Services","authors":"Shunhui Ji, Bixin Li, Pengcheng Zhang","doi":"10.1109/SCC.2016.77","DOIUrl":"https://doi.org/10.1109/SCC.2016.77","url":null,"abstract":"BPEL(Business Process Execution Language) composite service evolves a lot in its lifetime. Regression testing must be performed to ensure the correctness of each evolved version. In this article, an approach is proposed to select test cases for regression testing based on data flow testing criterion. With XCFG(eXtended Control Flow Graph) modeling BPEL composite service, the approach improves the traditional data flow analysis to compute the def-use pairs in BPEL process, and then identifies the affected def-use pairs by comparing the def-use pairs and XCFG model in the evolved version with those in the baseline version, where related WSDL(Web Service Description Language) documents are incorporated for comparison. The data flow paths covering the affected def-use pairs are calculated for regression testing, and some of them can reuse the test cases in the baseline version, which are determined by analyzing the path condition of data flow paths between two versions. The proposed approach can detect three kinds of changes, including process change, binding change and interface change. Experimental study shows the effectiveness.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126711313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jianxiao Liu, Zonglin Tian, Panbiao Liu, Jiawei Jiang, Zhao Li
How to classify and organize the semantic Web services to help users find the services to meet their needs quickly and accurately is a key issue to be solved in the era of service-oriented software engineering. This paper makes full use the characteristics of solid mathematical foundation and stable classification efficiency of naive bayes classification method. It proposes a semantic Web service classification method based on the theory of naive bayes. It elaborates the concrete process of how to use the three stages of bayesian classification to classify the semantic Web services in the consideration of service interface and execution capacity. The information gain theory is used to determine the classification influence of different features. Finally, the experiments are used to validate the proposed methods.
{"title":"An Approach of Semantic Web Service Classification Based on Naive Bayes","authors":"Jianxiao Liu, Zonglin Tian, Panbiao Liu, Jiawei Jiang, Zhao Li","doi":"10.1109/SCC.2016.53","DOIUrl":"https://doi.org/10.1109/SCC.2016.53","url":null,"abstract":"How to classify and organize the semantic Web services to help users find the services to meet their needs quickly and accurately is a key issue to be solved in the era of service-oriented software engineering. This paper makes full use the characteristics of solid mathematical foundation and stable classification efficiency of naive bayes classification method. It proposes a semantic Web service classification method based on the theory of naive bayes. It elaborates the concrete process of how to use the three stages of bayesian classification to classify the semantic Web services in the consideration of service interface and execution capacity. The information gain theory is used to determine the classification influence of different features. Finally, the experiments are used to validate the proposed methods.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124378759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Energy consumption is emerging as a new crucial issue of the Cloud Computing environments such as data centers. The problem of power consumption is more challenging especially in the context of scientific workflows deployment in the Cloud as they trigger intensive computational tasks and data manipulation steps which begets excessive data movement operations over communication networks. For instance, it was revealed that network devices consume up to one-third of the total energy consumption of Cloud data centers. In this paper, we propose an energy-aware approach for scientific workflows scheduling in the Cloud. In the first step, we propose a Workflow Partitioning for Energy Minimization (WPEM) algorithm that allows reducing the network energy consumption of the workflow and the total amount of data communication while achieving a high degree of parallelism. In the second step, we use the heuristic of Cat Swarm Optimization to schedule the generated partitions in order to minimize the workflow's overall energy consumption and execution time. We evaluated the proposed approach using three real cases of data intensive workflows and compare it with other algorithms from literature. The experimental results show that our proposal allows to reduce remarkably the network energy consumption of the tested workflows (up to 96% of network energy consumption saving for memory intensive workflows) and the overall energy consumption of the workflows while ensuring a reasonable execution time and using less Cloud resources.
能源消耗正在成为数据中心等云计算环境的一个新的关键问题。功耗问题更具挑战性，特别是在云中部署科学工作流的背景下，因为它们会触发密集的计算任务和数据操作步骤，从而在通信网络上产生过多的数据移动操作。例如，据透露，网络设备消耗了云数据中心总能耗的三分之一。在本文中，我们提出了一种在云中进行科学工作流调度的能量感知方法。首先，我们提出了一种工作流分区能量最小化(Workflow Partitioning for Energy Minimization, WPEM)算法，该算法在实现高并行度的同时，降低了工作流的网络能耗和数据通信总量。在第二步中，我们使用Cat群优化的启发式方法来调度生成的分区，以最小化工作流的总体能耗和执行时间。我们使用三个数据密集型工作流的真实案例评估了所提出的方法，并将其与文献中的其他算法进行了比较。实验结果表明，我们的方案在保证合理的执行时间和使用更少的云资源的同时，显著降低了被测工作流的网络能耗(内存密集型工作流的网络能耗节省高达96%)和工作流的整体能耗。
{"title":"Energy Efficient Partitioning and Scheduling Approach for Scientific Workflows in the Cloud","authors":"Khadija Bousselmi, Zaki Brahmi, M. Gammoudi","doi":"10.1109/SCC.2016.26","DOIUrl":"https://doi.org/10.1109/SCC.2016.26","url":null,"abstract":"Energy consumption is emerging as a new crucial issue of the Cloud Computing environments such as data centers. The problem of power consumption is more challenging especially in the context of scientific workflows deployment in the Cloud as they trigger intensive computational tasks and data manipulation steps which begets excessive data movement operations over communication networks. For instance, it was revealed that network devices consume up to one-third of the total energy consumption of Cloud data centers. In this paper, we propose an energy-aware approach for scientific workflows scheduling in the Cloud. In the first step, we propose a Workflow Partitioning for Energy Minimization (WPEM) algorithm that allows reducing the network energy consumption of the workflow and the total amount of data communication while achieving a high degree of parallelism. In the second step, we use the heuristic of Cat Swarm Optimization to schedule the generated partitions in order to minimize the workflow's overall energy consumption and execution time. We evaluated the proposed approach using three real cases of data intensive workflows and compare it with other algorithms from literature. The experimental results show that our proposal allows to reduce remarkably the network energy consumption of the tested workflows (up to 96% of network energy consumption saving for memory intensive workflows) and the overall energy consumption of the workflows while ensuring a reasonable execution time and using less Cloud resources.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123760901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Li Duan, Yang Zhang, Shiping Chen, Xuan Liu, B. Cheng, Junliang Chen
This paper presents a privacy disclosure recommendation approach based on a privacy cost model. The approach involves selecting appropriate credentials or attributes from users, and automatically building a new credential to fulfill service's authorization policies. The recommendation principles consider three aspects: (1) the selected user's attributes in the new credential satisfy the requested service's authorization policy, (2) hiding user's credentials and attributes to keep private during the request procedure, and (3) the total privacy cost of users is minimum. In addition, an automated tool is designed and implemented to derive a new credential. The correctness of our approach is demonstrated and validated by a practical case. Experimental results and complexity analysis show that our approach is efficient.
{"title":"Model-Based Minimum Privacy Disclosure Recommendation for Authorization Policies","authors":"Li Duan, Yang Zhang, Shiping Chen, Xuan Liu, B. Cheng, Junliang Chen","doi":"10.1109/SCC.2016.59","DOIUrl":"https://doi.org/10.1109/SCC.2016.59","url":null,"abstract":"This paper presents a privacy disclosure recommendation approach based on a privacy cost model. The approach involves selecting appropriate credentials or attributes from users, and automatically building a new credential to fulfill service's authorization policies. The recommendation principles consider three aspects: (1) the selected user's attributes in the new credential satisfy the requested service's authorization policy, (2) hiding user's credentials and attributes to keep private during the request procedure, and (3) the total privacy cost of users is minimum. In addition, an automated tool is designed and implemented to derive a new credential. The correctness of our approach is demonstrated and validated by a practical case. Experimental results and complexity analysis show that our approach is efficient.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121504497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：