Web-based social networking is increasingly gaining popularity due to the rapid development of computer networking technologies. However, social networking applications still cannot obtain a wider acceptance by many u...Web-based social networking is increasingly gaining popularity due to the rapid development of computer networking technologies. However, social networking applications still cannot obtain a wider acceptance by many users due to some unresolved issues, such as trust, security, and privacy. In social networks, trust is mainly studied whether a remote user behaves as expected by an interested user via other users, who are respectively named trustee, trustor, and recommenders. A trust graph consists of a trustor, a trustee, some recommenders, and the trust relationships between them. In this paper, we propose a novel FlowTrust approach to model a trust graph with network flows, and evaluate the maximum amount of trust that can flow through a trust graph using network flow theory. FlowTrust supports multi-dimensional trust. We use trust value and confidence level as two trust factors. We deduce four trust metrics from these two trust factors, which are maximum flow of trust value, maximum flow of confidence level, minimum cost of uncertainty with maximum flow of trust, and minimum cost of mistrust with maximum flow of confidence. We also propose three FlowTrust algorithms to normalize these four trust metrics. We compare our proposed FlowTrust approach with the existing RelTrust and CircuitTrust approaches. We show that all three approaches are comparable in terms of the inferred trust values. Therefore, FlowTrust is the best of the three since it also supports multi-dimensional trust.展开更多
The trust as a social relationship captures similarity of tastes or interests in perspective. However, the existent trust information is usually very sparse, which may suppress the accuracy of our personal product rec...The trust as a social relationship captures similarity of tastes or interests in perspective. However, the existent trust information is usually very sparse, which may suppress the accuracy of our personal product recommendation algorithm via a listening and trust preference network. Based on this thinking, we experiment the typical trust inference methods to find out the most excellent friend-recommending index which is used to expand the current trust network. Experimental results demonstrate the expanded friendships via superposed random walk can indeed improve the accuracy of our personal product recommendation.展开更多
The recent and unprecedented surge of public interest in peer-to-peer (P2P) file-sharing systems has led to a variety of interesting research questions. How to minimize threats in such an open community is an impor-ta...The recent and unprecedented surge of public interest in peer-to-peer (P2P) file-sharing systems has led to a variety of interesting research questions. How to minimize threats in such an open community is an impor-tant research topic. Trust models have been widely used in estimating the trustworthiness of peers in P2P file-sharing systems where peers can transact with each other without prior experience. However, current P2P trust models almost take no consideration for the nature of trust, fuzzy, complex and dynamic, which results in low efficiency in resisting the attacks of malicious nodes. In this paper, a new trust model named NatureTrust that can alleviate the shortage brought by the nature of trust is proposed. In order to cope with the fuzzy characteristic of trust, linguistic terms are used to express trust. Additionally, fuzzy inference rules are employed to evaluate trust of each transaction so as to handle the complex characteristic of trust. Fur-thermore, risk factor is deployed into NatureTrust to represent and reason with the dynamic characteristic of trust. Both risk and trust factors are considered in evaluating the trustworthiness of each peer. Experimental results show that the trust model analyzed here thus stands against malicious act effectively.展开更多
为了简化PKI信任模型的逻辑推理,本文对Bakkali H E和Kaitouni B I提出的两种推理方法进行了改进。首先通过使用三个谓词和三个约束条件给出与信任模型相关的定义和推理规则,并在此基础上提出了两个推论。可以证明,该方法能够有效地简...为了简化PKI信任模型的逻辑推理,本文对Bakkali H E和Kaitouni B I提出的两种推理方法进行了改进。首先通过使用三个谓词和三个约束条件给出与信任模型相关的定义和推理规则,并在此基础上提出了两个推论。可以证明,该方法能够有效地简化逻辑推理。最后,在层次结构模型中分析其推理过程。展开更多
文摘Web-based social networking is increasingly gaining popularity due to the rapid development of computer networking technologies. However, social networking applications still cannot obtain a wider acceptance by many users due to some unresolved issues, such as trust, security, and privacy. In social networks, trust is mainly studied whether a remote user behaves as expected by an interested user via other users, who are respectively named trustee, trustor, and recommenders. A trust graph consists of a trustor, a trustee, some recommenders, and the trust relationships between them. In this paper, we propose a novel FlowTrust approach to model a trust graph with network flows, and evaluate the maximum amount of trust that can flow through a trust graph using network flow theory. FlowTrust supports multi-dimensional trust. We use trust value and confidence level as two trust factors. We deduce four trust metrics from these two trust factors, which are maximum flow of trust value, maximum flow of confidence level, minimum cost of uncertainty with maximum flow of trust, and minimum cost of mistrust with maximum flow of confidence. We also propose three FlowTrust algorithms to normalize these four trust metrics. We compare our proposed FlowTrust approach with the existing RelTrust and CircuitTrust approaches. We show that all three approaches are comparable in terms of the inferred trust values. Therefore, FlowTrust is the best of the three since it also supports multi-dimensional trust.
文摘The trust as a social relationship captures similarity of tastes or interests in perspective. However, the existent trust information is usually very sparse, which may suppress the accuracy of our personal product recommendation algorithm via a listening and trust preference network. Based on this thinking, we experiment the typical trust inference methods to find out the most excellent friend-recommending index which is used to expand the current trust network. Experimental results demonstrate the expanded friendships via superposed random walk can indeed improve the accuracy of our personal product recommendation.
文摘The recent and unprecedented surge of public interest in peer-to-peer (P2P) file-sharing systems has led to a variety of interesting research questions. How to minimize threats in such an open community is an impor-tant research topic. Trust models have been widely used in estimating the trustworthiness of peers in P2P file-sharing systems where peers can transact with each other without prior experience. However, current P2P trust models almost take no consideration for the nature of trust, fuzzy, complex and dynamic, which results in low efficiency in resisting the attacks of malicious nodes. In this paper, a new trust model named NatureTrust that can alleviate the shortage brought by the nature of trust is proposed. In order to cope with the fuzzy characteristic of trust, linguistic terms are used to express trust. Additionally, fuzzy inference rules are employed to evaluate trust of each transaction so as to handle the complex characteristic of trust. Fur-thermore, risk factor is deployed into NatureTrust to represent and reason with the dynamic characteristic of trust. Both risk and trust factors are considered in evaluating the trustworthiness of each peer. Experimental results show that the trust model analyzed here thus stands against malicious act effectively.
