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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Guangming Yang Liangyu Zhang Zhenhua Tan Hainan Yu Shuang Li |
| Copyright Year | 2012 |
| Abstract | Trust is applied to different aspects in a variety of systems. In computing systems, it is not well understood from computational perspective. Knowing how much a person trusts the others could be helpful in obtaining the trusted group in peer-to-peer network and trusted computing is a good way to measure the trust. Trust inference is essential in decision making. Conditional probability is used to quantize the trust relationship. This paper proposes a new algorithm based on the improvements of Markov model and adopts the level factor and confidence to compute the indirect trust inference. Through replicating cross-nodes, the trust network could consist of several independent paths without duplicate nodes. This algorithm describes two facts that with the increase of indirect relationship, the trust value should be lower, and each person has his own confidence in trust probability. In our experiment on the dataset Epinion, compared with the well-known trust inference algorithms Average and Multiplication, our method computes a more accurate and objective trust inference value, demonstrating its effectiveness. |
| Starting Page | 349 |
| Ending Page | 354 |
| File Size | 386620 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781467348737 |
| DOI | 10.1109/CIT.2012.87 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-10-27 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Peer to peer computing Computational modeling Social network services Markov confidence Markov processes Inference algorithms level factor Mathematical model History trust inference peer-to-peer network |
| Content Type | Text |
| Resource Type | Article |
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