Parameter learning of personalized trust models in broker-based distributed trust management

Jane Yung Jen Hsu*, Kwei Jay Lin, Tsung Hsiang Chang, Chien Ju Ho, Han Shen Huang, Wan Rong Jih

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

13 Scopus citations


Distributed 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.

Original languageEnglish
Pages (from-to)321-333
Number of pages13
JournalInformation Systems Frontiers
Issue number4
StatePublished - 09 2006
Externally publishedYes


  • Distributed trust management
  • Expectation maximization
  • Parameter learning
  • Personalized feedback rating
  • Probabilistic trust model
  • Reputation mechanism


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