Recent developments in cloud architectures and security concerns have originated new models of online storage clouds based on data dispersal algorithms. According to these lgorithms the data is divided into several slices that are distributed among remote and independent storage nodes. Ensuring confidentiality in this context is crucial: only legitimate users should access any part of information they distribute among storage nodes. We use parameterized Markov Decision Processes to model such a class of systems and Probabilistic Model Checking to assess the likelihood of breaking the confidentiality.We showed that a Small Model Theorem can be proven for a specific types of models, preserving PCTL formulae. Finally, we report the result of applying our methodology to feasibly assess the security of existing dispersed cloud storage solutions.

A probabilistic small model theorem to assess confidentiality of dispersed cloud storage (extended abstract) / Baldi, Marco; Bartocci, Ezio; Chiaraluce, Franco; Cucchiarelli, Alessandro; Senigagliesi, Linda; Spalazzi, Luca; Spegni, Francesco. - ELETTRONICO. - 1949:(2017), pp. 208-212. (Intervento presentato al convegno Joint 18th Italian Conference on Theoretical Computer Science and the 32nd Italian Conference on Computational Logic tenutosi a Naples nel 26-28 September 2017).

A probabilistic small model theorem to assess confidentiality of dispersed cloud storage (extended abstract)

Marco Baldi;BARTOCCI, EZIO;Franco Chiaraluce;Alessandro Cucchiarelli;Linda Senigagliesi;Luca Spalazzi;and Francesco Spegni
2017-01-01

Abstract

Recent developments in cloud architectures and security concerns have originated new models of online storage clouds based on data dispersal algorithms. According to these lgorithms the data is divided into several slices that are distributed among remote and independent storage nodes. Ensuring confidentiality in this context is crucial: only legitimate users should access any part of information they distribute among storage nodes. We use parameterized Markov Decision Processes to model such a class of systems and Probabilistic Model Checking to assess the likelihood of breaking the confidentiality.We showed that a Small Model Theorem can be proven for a specific types of models, preserving PCTL formulae. Finally, we report the result of applying our methodology to feasibly assess the security of existing dispersed cloud storage solutions.
2017
CEUR Workshop Proceedings
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/252391
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact