A probabilistic small model theorem to assess confidentiality of dispersed cloud storage

Abstract

Recent developments in cloud architectures have originated new models of online storage clouds based on data dispersal algorithms. According to these algorithms 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. To the best of our knowledge, the security analysis and assessment of existing solutions always assume homogeneous networks and honest-but-curious nodes as attacker model. We analyze more complex scenarios with heterogeneous network topologies and a passive attacker eavesdropping the channel between user and 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. Even if, generally speaking, the parameterized model checking is undecidable, in this paper, however, we proved a Small Model Theorem that makes such a problem decidable for the class of models adopted in this work. We discovered that confidentiality is highly affected by parameters such as the number of slices and the number of write and read requests. At design-time, the presented methodology helps to determine the optimal values of parameters affecting the likelihood of a successful attack to confidentiality.