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On the Application of Supervised Machine Learning to Trustworthiness Assessment

Hauke, Sascha and Biedermann, Sebastian and Heider, Dominik and Mühlhäuser, Max (2013):
On the Application of Supervised Machine Learning to Trustworthiness Assessment.
(TR-014), DOI: 10.1109/TrustCom.2013.5,
[Report]

Abstract

State-of-the art trust and reputation systems seek to apply machine learning methods to overcome generalizability issues of experience-based Bayesian trust assessment. These approaches are, however, often model-centric instead of focussing on data and the complex adaptive system that is driven by reputation-based service selection. This entails the risk of unrealistic model assumptions. We outline the requirements for robust probabilistic trust assessment using supervised learning and apply a selection of estimators to a real-world dataset, in order to show the effectiveness of supervised methods. Furthermore, we provide a representational mapping of estimator output to a belief logic representation for the modular integration of supervised methods with other trust assessment methodologies.

Item Type: Report
Erschienen: 2013
Creators: Hauke, Sascha and Biedermann, Sebastian and Heider, Dominik and Mühlhäuser, Max
Title: On the Application of Supervised Machine Learning to Trustworthiness Assessment
Language: German
Abstract:

State-of-the art trust and reputation systems seek to apply machine learning methods to overcome generalizability issues of experience-based Bayesian trust assessment. These approaches are, however, often model-centric instead of focussing on data and the complex adaptive system that is driven by reputation-based service selection. This entails the risk of unrealistic model assumptions. We outline the requirements for robust probabilistic trust assessment using supervised learning and apply a selection of estimators to a real-world dataset, in order to show the effectiveness of supervised methods. Furthermore, we provide a representational mapping of estimator output to a belief logic representation for the modular integration of supervised methods with other trust assessment methodologies.

Number: TR-014
Uncontrolled Keywords: - SST: CASED:;- SST - Area Smart Security and Trust
Divisions: LOEWE > LOEWE-Zentren > CASED – Center for Advanced Security Research Darmstadt
20 Department of Computer Science > Telecooperation
LOEWE > LOEWE-Zentren
20 Department of Computer Science
LOEWE
Date Deposited: 31 Dec 2016 12:59
DOI: 10.1109/TrustCom.2013.5
Identification Number: TUD-CS-2013-0050
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