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)
BARTOCCI, EZIO;and Francesco Spegni
2017
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
