YUNG-JEN HSULin, Kwei-JayKwei-JayLinChang, Tsung-HsiangTsung-HsiangChangHo, Chien-juChien-juHoHuang, Han-ShenHan-ShenHuangJih, Wan-rongWan-rongJih2009-04-292018-07-052009-04-292018-07-05200613873326http://ntur.lib.ntu.edu.tw//handle/246246/154538https://www.scopus.com/inward/record.uri?eid=2-s2.0-33845495174&doi=10.1007%2fs10796-006-9005-4&partnerID=40&md5=cb883b31eaabe56032b0e8d6ca33bbb9Distributed trust management addresses the challenges of eliciting, evaluating and propagating trust for service providers on the distributed network. By delegating trust management to brokers, individual users can share their feedbacks for services without the overhead of maintaining their own ratings. This research proposes a two-tier trust hierarchy, in which a user relies on her broker to provide reputation rating about any service provider, while brokers leverage their connected partners in aggregating the reputation of unfamiliar service providers. Each broker collects feedbacks from its users on past transactions. To accommodate individual differences, personalized trust is modeled with a Bayesian network. Training strategies such as the expectation maximization (EM) algorithm can be deployed to estimate both server reputation and user bias. This paper presents the design and implementation of a distributed trust simulator, which supports experiments under different configurations. In addition, we have conducted experiments to show the following. 1) Personal rating error converges to below 5% consistently within 10,000 transactions regardless of the training strategy or bias distribution. 2) The choice of trust model has a significant impact on the performance of reputation prediction. 3) The two-tier trust framework scales well to distributed environments. In summary, parameter learning of trust models in the broker-based framework enables both aggregation of feedbacks and personalized reputation prediction. © Springer Science+Business Media, LLC 2006.application/pdf526416 bytesapplication/pdfen-USDistributed trust management; Expectation maximization; Parameter learning; Personalized feedback rating; Probabilistic trust model; Reputation mechanismData structures; Feedback; Information services; Learning systems; Probabilistic logics; Security of data; Distributed trust management; Parameter learning; Probabilistic trust models; Reputation mechanism; Distributed database systemsjournal article2-s2.0-33845495174http://ntur.lib.ntu.edu.tw/bitstream/246246/154538/1/13.pdf