The Experts below are selected from a list of 804 Experts worldwide ranked by ideXlab platform
Yindong Chen - One of the best experts on this subject based on the ideXlab platform.
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An Online Personalized Reputation Estimation Model for Service-Oriented Systems
2019 IEEE International Conference on Services Computing (SCC), 2019Co-Authors: Jianlong Xu, Xin Du, Yindong ChenAbstract:In service-oriented computing environments, many Web services are provided for Users to build service-oriented systems. Since the performance of the same Web service is different from different Users' perspectives, Users have to personally select the optimal Web services according to quality-of-service(QoS) data observed by other similar Users. However, Users with low Reputations will provide unreliable data, which will have a negative impact on service selection. Moreover, the QoS data vary over time due to changes in User Reputation. Therefore, how to estimate a personalized Reputation for each User at runtime remains a significant problem. To address this critical challenge, this paper proposes an online Reputation estimation method, called OPRE, to efficiently provide a personalized Reputation for each User. Based on the Users' observed QoS data, OPRE employs matrix factorization and online learning techniques to estimate personalized Reputations. The experimental results show that OPRE has high effectiveness compared to other approaches.
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meurep a novel User Reputation calculation approach in personalized cloud services
PLOS ONE, 2019Co-Authors: Jianlong Xu, Xin Du, Yindong ChenAbstract:: User reliability is notably crucial for personalized cloud services. In cloud computing environments, large amounts of cloud services are provided for Users. With the exponential increase in number of cloud services, it is difficult for Users to select the appropriate services from equivalent or similar candidate services. The quality-of-service (QoS) has become an important criterion for selection, and the Users can conduct personalized selection according to the observed QoS data of other Users; however, it is difficult to ensure that the Users are reliable. Actually, unreliable Users may provide unreliable QoS data and have negative effects on the personalized cloud service selection. Therefore, how to determine reliable QoS data for personalized cloud service selection remains a significant problem. To measure the reliability for each User, we present a cloud service selection framework based on User Reputation and propose a new User Reputation calculation approach, which is named MeURep and includes L1-MeURep and L2-MeURep. Experiments are conducted, and the results confirm that MeURep has higher efficiency than previously proposed approaches.
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OPRC: An Online Personalized Reputation Calculation Model in Service-Oriented Computing Environments
IEEE Access, 2019Co-Authors: Xin Du, Jianlong Xu, Yindong ChenAbstract:Cloud applications based on service-oriented architectures usually integrate many component services to implement specific application logic. In service-oriented computing environments, many Web services are provided for Users to build service-oriented systems. Since the performance of the same Web service varies according to different Users' perspectives, the Users have to personally select the optimal Web services according to the quality-of-service (QoS) data observed by other similar Users. However, Users with a low Reputation provide unreliable data, which has a negative impact on service selection. Moreover, the QoS data vary over time due to changes in User Reputation; and therefore, how to calculate a personalized Reputation for each User at runtime remains a substantial problem. To address this critical challenge, this paper proposes an online Reputation calculation method, called the OPRC, to efficiently provide a personalized Reputation for each User. Based on the Users' observed QoS data, the OPRC employs MF and online learning techniques to calculate personalized Reputations. To validate the approach, large-scale experiments are conducted, which contain two QoS attributes from 142 reliable Users and 15 unreliable Users. The results show that OPRC has high accuracy and effectiveness compared to other approaches.
Nicholas Hopper - One of the best experts on this subject based on the ideXlab platform.
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NDSS - rBridge: User Reputation based Tor Bridge Distribution with Privacy Preservation.
2020Co-Authors: Qiyan Wang, Nikita Borisov, Nicholas HopperAbstract:Tor is one of the most popular censorship circumvention systems; it uses bridges run by volunteers as proxies to evade censorship. A key challenge to the Tor circumvention system is to distribute bridges to a large number of Users while avoiding having the bridges fall into the hands of corrupt Users. We propose rBridge—a User Reputation system for bridge distribution; it assigns bridges according to the past history of Users to limit corrupt Users from repeatedly blocking bridges, and employs an introduction-based mechanism to invite new Users while resisting Sybil attacks. Our evaluation results show that rBridge provides much stronger protection for bridges than any existing scheme. We also address another important challenge to the bridge distribution—preserving the privacy of Users’ bridge assignment information, which can be exploited by malicious parties to degrade Users’ anonymity in anonymous communication.
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rbridge User Reputation based tor bridge distribution with privacy preservation
Network and Distributed System Security Symposium, 2013Co-Authors: Qiyan Wang, Nikita Borisov, Nicholas HopperAbstract:Tor is one of the most popular censorship circumvention systems; it uses bridges run by volunteers as proxies to evade censorship. A key challenge to the Tor circumvention system is to distribute bridges to a large number of Users while avoiding having the bridges fall into the hands of corrupt Users. We propose rBridge—a User Reputation system for bridge distribution; it assigns bridges according to the past history of Users to limit corrupt Users from repeatedly blocking bridges, and employs an introduction-based mechanism to invite new Users while resisting Sybil attacks. Our evaluation results show that rBridge provides much stronger protection for bridges than any existing scheme. We also address another important challenge to the bridge distribution—preserving the privacy of Users’ bridge assignment information, which can be exploited by malicious parties to degrade Users’ anonymity in anonymous communication.
Jianlong Xu - One of the best experts on this subject based on the ideXlab platform.
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An Online Personalized Reputation Estimation Model for Service-Oriented Systems
2019 IEEE International Conference on Services Computing (SCC), 2019Co-Authors: Jianlong Xu, Xin Du, Yindong ChenAbstract:In service-oriented computing environments, many Web services are provided for Users to build service-oriented systems. Since the performance of the same Web service is different from different Users' perspectives, Users have to personally select the optimal Web services according to quality-of-service(QoS) data observed by other similar Users. However, Users with low Reputations will provide unreliable data, which will have a negative impact on service selection. Moreover, the QoS data vary over time due to changes in User Reputation. Therefore, how to estimate a personalized Reputation for each User at runtime remains a significant problem. To address this critical challenge, this paper proposes an online Reputation estimation method, called OPRE, to efficiently provide a personalized Reputation for each User. Based on the Users' observed QoS data, OPRE employs matrix factorization and online learning techniques to estimate personalized Reputations. The experimental results show that OPRE has high effectiveness compared to other approaches.
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meurep a novel User Reputation calculation approach in personalized cloud services
PLOS ONE, 2019Co-Authors: Jianlong Xu, Xin Du, Yindong ChenAbstract:: User reliability is notably crucial for personalized cloud services. In cloud computing environments, large amounts of cloud services are provided for Users. With the exponential increase in number of cloud services, it is difficult for Users to select the appropriate services from equivalent or similar candidate services. The quality-of-service (QoS) has become an important criterion for selection, and the Users can conduct personalized selection according to the observed QoS data of other Users; however, it is difficult to ensure that the Users are reliable. Actually, unreliable Users may provide unreliable QoS data and have negative effects on the personalized cloud service selection. Therefore, how to determine reliable QoS data for personalized cloud service selection remains a significant problem. To measure the reliability for each User, we present a cloud service selection framework based on User Reputation and propose a new User Reputation calculation approach, which is named MeURep and includes L1-MeURep and L2-MeURep. Experiments are conducted, and the results confirm that MeURep has higher efficiency than previously proposed approaches.
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OPRC: An Online Personalized Reputation Calculation Model in Service-Oriented Computing Environments
IEEE Access, 2019Co-Authors: Xin Du, Jianlong Xu, Yindong ChenAbstract:Cloud applications based on service-oriented architectures usually integrate many component services to implement specific application logic. In service-oriented computing environments, many Web services are provided for Users to build service-oriented systems. Since the performance of the same Web service varies according to different Users' perspectives, the Users have to personally select the optimal Web services according to the quality-of-service (QoS) data observed by other similar Users. However, Users with a low Reputation provide unreliable data, which has a negative impact on service selection. Moreover, the QoS data vary over time due to changes in User Reputation; and therefore, how to calculate a personalized Reputation for each User at runtime remains a substantial problem. To address this critical challenge, this paper proposes an online Reputation calculation method, called the OPRC, to efficiently provide a personalized Reputation for each User. Based on the Users' observed QoS data, the OPRC employs MF and online learning techniques to calculate personalized Reputations. To validate the approach, large-scale experiments are conducted, which contain two QoS attributes from 142 reliable Users and 15 unreliable Users. The results show that OPRC has high accuracy and effectiveness compared to other approaches.
Qiyan Wang - One of the best experts on this subject based on the ideXlab platform.
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NDSS - rBridge: User Reputation based Tor Bridge Distribution with Privacy Preservation.
2020Co-Authors: Qiyan Wang, Nikita Borisov, Nicholas HopperAbstract:Tor is one of the most popular censorship circumvention systems; it uses bridges run by volunteers as proxies to evade censorship. A key challenge to the Tor circumvention system is to distribute bridges to a large number of Users while avoiding having the bridges fall into the hands of corrupt Users. We propose rBridge—a User Reputation system for bridge distribution; it assigns bridges according to the past history of Users to limit corrupt Users from repeatedly blocking bridges, and employs an introduction-based mechanism to invite new Users while resisting Sybil attacks. Our evaluation results show that rBridge provides much stronger protection for bridges than any existing scheme. We also address another important challenge to the bridge distribution—preserving the privacy of Users’ bridge assignment information, which can be exploited by malicious parties to degrade Users’ anonymity in anonymous communication.
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rbridge User Reputation based tor bridge distribution with privacy preservation
Network and Distributed System Security Symposium, 2013Co-Authors: Qiyan Wang, Nikita Borisov, Nicholas HopperAbstract:Tor is one of the most popular censorship circumvention systems; it uses bridges run by volunteers as proxies to evade censorship. A key challenge to the Tor circumvention system is to distribute bridges to a large number of Users while avoiding having the bridges fall into the hands of corrupt Users. We propose rBridge—a User Reputation system for bridge distribution; it assigns bridges according to the past history of Users to limit corrupt Users from repeatedly blocking bridges, and employs an introduction-based mechanism to invite new Users while resisting Sybil attacks. Our evaluation results show that rBridge provides much stronger protection for bridges than any existing scheme. We also address another important challenge to the bridge distribution—preserving the privacy of Users’ bridge assignment information, which can be exploited by malicious parties to degrade Users’ anonymity in anonymous communication.
Xin Du - One of the best experts on this subject based on the ideXlab platform.
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An Online Personalized Reputation Estimation Model for Service-Oriented Systems
2019 IEEE International Conference on Services Computing (SCC), 2019Co-Authors: Jianlong Xu, Xin Du, Yindong ChenAbstract:In service-oriented computing environments, many Web services are provided for Users to build service-oriented systems. Since the performance of the same Web service is different from different Users' perspectives, Users have to personally select the optimal Web services according to quality-of-service(QoS) data observed by other similar Users. However, Users with low Reputations will provide unreliable data, which will have a negative impact on service selection. Moreover, the QoS data vary over time due to changes in User Reputation. Therefore, how to estimate a personalized Reputation for each User at runtime remains a significant problem. To address this critical challenge, this paper proposes an online Reputation estimation method, called OPRE, to efficiently provide a personalized Reputation for each User. Based on the Users' observed QoS data, OPRE employs matrix factorization and online learning techniques to estimate personalized Reputations. The experimental results show that OPRE has high effectiveness compared to other approaches.
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meurep a novel User Reputation calculation approach in personalized cloud services
PLOS ONE, 2019Co-Authors: Jianlong Xu, Xin Du, Yindong ChenAbstract:: User reliability is notably crucial for personalized cloud services. In cloud computing environments, large amounts of cloud services are provided for Users. With the exponential increase in number of cloud services, it is difficult for Users to select the appropriate services from equivalent or similar candidate services. The quality-of-service (QoS) has become an important criterion for selection, and the Users can conduct personalized selection according to the observed QoS data of other Users; however, it is difficult to ensure that the Users are reliable. Actually, unreliable Users may provide unreliable QoS data and have negative effects on the personalized cloud service selection. Therefore, how to determine reliable QoS data for personalized cloud service selection remains a significant problem. To measure the reliability for each User, we present a cloud service selection framework based on User Reputation and propose a new User Reputation calculation approach, which is named MeURep and includes L1-MeURep and L2-MeURep. Experiments are conducted, and the results confirm that MeURep has higher efficiency than previously proposed approaches.
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OPRC: An Online Personalized Reputation Calculation Model in Service-Oriented Computing Environments
IEEE Access, 2019Co-Authors: Xin Du, Jianlong Xu, Yindong ChenAbstract:Cloud applications based on service-oriented architectures usually integrate many component services to implement specific application logic. In service-oriented computing environments, many Web services are provided for Users to build service-oriented systems. Since the performance of the same Web service varies according to different Users' perspectives, the Users have to personally select the optimal Web services according to the quality-of-service (QoS) data observed by other similar Users. However, Users with a low Reputation provide unreliable data, which has a negative impact on service selection. Moreover, the QoS data vary over time due to changes in User Reputation; and therefore, how to calculate a personalized Reputation for each User at runtime remains a substantial problem. To address this critical challenge, this paper proposes an online Reputation calculation method, called the OPRC, to efficiently provide a personalized Reputation for each User. Based on the Users' observed QoS data, the OPRC employs MF and online learning techniques to calculate personalized Reputations. To validate the approach, large-scale experiments are conducted, which contain two QoS attributes from 142 reliable Users and 15 unreliable Users. The results show that OPRC has high accuracy and effectiveness compared to other approaches.
