Paper Info
Title
Parametric and Probabilistic Model Checking of Confidentiality in Data Dispersal Algorithms (Extended Version).
Abstract
Recent developments in cloud storage architectures have originated new models of online storage as cooperative storage systems and interconnected clouds. Such distributed environments involve many organizations, thus ensuring confidentiality becomes crucial: only legitimate clients should recover the information they distribute among storage nodes. In this work we present a unified framework for verifying confidentiality of dispersal algorithms against probabilistic models of intruders. Two models of intruders are given, corresponding to different types of attackers: one aiming at intercepting as many slices of information as possible, and the other aiming at attacking the storage providers in the network. Both try to recover the original information, given the intercepted slices. By using probabilistic model checking, we can measure the degree of confidentiality of the system exploring exhaustively all possible behaviors. Our experiments suggest that dispersal algorithms ensure a high degree of confidentiality against the slice intruder, no matter the number of storage providers in the system. On the contrary, they show a low level of confidentiality against the provider intruder in networks with few storage providers (e.g. interconnected cloud storage solutions).
Year
Venue
Field
2016
arXiv: Cryptography and Security
Confidentiality,Computer security,Computer science,Algorithm,Parametric statistics,Probabilistic logic,Biological dispersal,Cloud storage,Probabilistic model checking,Distributed computing
DocType
Volume
Citations 
Journal
abs/1605.05930
0
PageRank 
References 
Authors
0.34
7
5
Name
Order
Citations
PageRank
Marco Baldi129937.07
Alessandro Cucchiarelli222636.38
Linda Senigagliesi323.10
Luca Spalazzi419734.55
F. Spegni5186.01