Ries, Sebastian ; Fischlin, Marc ; Martucci, Leonardo ; Mühlhäuser, Max (2011)
Learning Whom to Trust in a Privacy-Friendly Way.
10th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom-11). Changsha, People's Republic of China (16.-18.11.2011)
doi: 10.1109/TrustCom.2011.30
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2011 |
| Autor(en): | Ries, Sebastian ; Fischlin, Marc ; Martucci, Leonardo ; Mühlhäuser, Max |
| Art des Eintrags: | Bibliographie |
| Titel: | Learning Whom to Trust in a Privacy-Friendly Way |
| Sprache: | Englisch |
| Publikationsjahr: | 2011 |
| Verlag: | IEEE |
| Buchtitel: | Proceedings of the 10th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom-11) |
| Veranstaltungstitel: | 10th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom-11) |
| Veranstaltungsort: | Changsha, People's Republic of China |
| Veranstaltungsdatum: | 16.-18.11.2011 |
| DOI: | 10.1109/TrustCom.2011.30 |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | Secure Services;- SST: CASED:;- SST - Area Smart Security and Trust |
| ID-Nummer: | TUD-CS-2011-0234 |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Telekooperation LOEWE LOEWE > LOEWE-Zentren LOEWE > LOEWE-Zentren > CASED – Center for Advanced Security Research Darmstadt |
| Hinterlegungsdatum: | 31 Dez 2016 12:59 |
| Letzte Änderung: | 02 Nov 2021 10:07 |
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