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Learning Whom to Trust in a Privacy-Friendly Way

Ries, Sebastian and Fischlin, Marc and Martucci, Leonardo and Mühlhäuser, Max (2011):
Learning Whom to Trust in a Privacy-Friendly Way.
In: Proceedings of the 10th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom-11), [Conference or Workshop Item]

Abstract

The topics of trust and privacy are more relevant to users of online communities than ever be- fore. Trust models provide excellent means for sup- porting users in their decision making process. How- ever, those models require an exchange of information between users, which can pose a threat to the users' privacy. In this paper, we present a novel approach for a privacy preserving computation of trust. Besides pre- serving the privacy of the recommenders by exchanging and aggregating recommendations under encryption, the proposed approach is the first that enables the trusting entities to learn about the trustworthiness of their recommenders at the same time. This is achieved by linking the minimum amount of information that is required for the learning process to the actual rec- ommendation and by using zero-knowledge proofs for assuring the correctness of this additional information.

Item Type: Conference or Workshop Item
Erschienen: 2011
Creators: Ries, Sebastian and Fischlin, Marc and Martucci, Leonardo and Mühlhäuser, Max
Title: Learning Whom to Trust in a Privacy-Friendly Way
Language: German
Abstract:

The topics of trust and privacy are more relevant to users of online communities than ever be- fore. Trust models provide excellent means for sup- porting users in their decision making process. How- ever, those models require an exchange of information between users, which can pose a threat to the users' privacy. In this paper, we present a novel approach for a privacy preserving computation of trust. Besides pre- serving the privacy of the recommenders by exchanging and aggregating recommendations under encryption, the proposed approach is the first that enables the trusting entities to learn about the trustworthiness of their recommenders at the same time. This is achieved by linking the minimum amount of information that is required for the learning process to the actual rec- ommendation and by using zero-knowledge proofs for assuring the correctness of this additional information.

Title of Book: Proceedings of the 10th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom-11)
Uncontrolled Keywords: Secure Services;- 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
Identification Number: TUD-CS-2011-0234
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