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A Probabilistic Small Model Theorem to Assess Confidentiality of Dispersed Cloud Storage

  • Marco Baldi
  • Ezio Bartocci
  • Franco Chiaraluce
  • Alessandro Cucchiarelli
  • Linda Senigagliesi
  • Luca Spalazzi
  • Francesco Spegni
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10503)

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.

Notes

Acknowledgements

This work is part of the project FCloSe (Federated Cloud Security) funded by the RSA-B 2015 programme of the Università Politecnica delle Marche. Ezio Bartocci is supported by the Austrian National Research Network (nr. S 11405-N23) SHiNE funded by the Austrian Science Fund (FWF).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Marco Baldi
    • 1
  • Ezio Bartocci
    • 2
  • Franco Chiaraluce
    • 1
  • Alessandro Cucchiarelli
    • 1
  • Linda Senigagliesi
    • 1
  • Luca Spalazzi
    • 1
  • Francesco Spegni
    • 1
  1. 1.Università Politecnica delle MarcheAnconaItaly
  2. 2.TU WienViennaAustria

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