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Evidence-Based Trust Mechanism Using Clustering Algorithms for Distributed Storage Systems

Traverso, Giulia ; Cordero, Carlos Garcia ; Nojoumian, Mehrdad ; Azarderakhsh, Reza ; Demirel, Denise ; Habib, Sheikh Mahbub ; Buchmann, Johannes :
Evidence-Based Trust Mechanism Using Clustering Algorithms for Distributed Storage Systems.
[Online-Edition: https://www.ucalgary.ca/pst2017/]
15th Annual Conference on Privacy, Security and Trust (PST)
[ Konferenzveröffentlichung] , (2017)

Offizielle URL: https://www.ucalgary.ca/pst2017/

Kurzbeschreibung (Abstract)

In distributed storage systems, documents are shared among multiple Cloud providers and stored within their respective storage servers. In social secret sharing-based distributed storage systems, shares of the documents are allocated according to the trustworthiness of the storage servers. This paper proposes a trust mechanism using machine learning techniques to compute evidence-based trust values. Our mechanism mitigates the effect of colluding storage servers. More precisely, it becomes possible to detect unreliable evidence and establish countermeasures in order to discourage the collusion of storage servers. Furthermore, this trust mechanism is applied to the social secret sharing protocol AS3, showing that this new evidence-based trust mechanism enhances the protection of the stored documents.

Typ des Eintrags: Konferenzveröffentlichung ( nicht bekannt)
Erschienen: 2017
Autor(en): Traverso, Giulia ; Cordero, Carlos Garcia ; Nojoumian, Mehrdad ; Azarderakhsh, Reza ; Demirel, Denise ; Habib, Sheikh Mahbub ; Buchmann, Johannes
Titel: Evidence-Based Trust Mechanism Using Clustering Algorithms for Distributed Storage Systems
Sprache: Englisch
Kurzbeschreibung (Abstract):

In distributed storage systems, documents are shared among multiple Cloud providers and stored within their respective storage servers. In social secret sharing-based distributed storage systems, shares of the documents are allocated according to the trustworthiness of the storage servers. This paper proposes a trust mechanism using machine learning techniques to compute evidence-based trust values. Our mechanism mitigates the effect of colluding storage servers. More precisely, it becomes possible to detect unreliable evidence and establish countermeasures in order to discourage the collusion of storage servers. Furthermore, this trust mechanism is applied to the social secret sharing protocol AS3, showing that this new evidence-based trust mechanism enhances the protection of the stored documents.

Buchtitel: 15th Annual Conference on Privacy, Security and Trust (PST)
Freie Schlagworte: Solutions; S6; SPIN: Smart Protection in Infrastructures and Networks
Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Theoretische Informatik - Kryptographie und Computeralgebra
20 Fachbereich Informatik > Telekooperation
DFG-Sonderforschungsbereiche (inkl. Transregio)
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche
Profilbereiche
Profilbereiche > Cybersicherheit (CYSEC)
LOEWE
LOEWE > LOEWE-Zentren
LOEWE > LOEWE-Zentren > CRISP - Center for Research in Security and Privacy
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1119: CROSSING – Kryptographiebasierte Sicherheitslösungen als Grundlage für Vertrauen in heutigen und zukünftigen IT-Systemen
Veranstaltungsort: Calgary, CA
Veranstaltungsdatum: 28.-30.8. 2017
Hinterlegungsdatum: 05 Sep 2017 10:35
Offizielle URL: https://www.ucalgary.ca/pst2017/
ID-Nummer: TUD-CS-2017-0237
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