Approximate Persistent Stochastic Non Interference

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dc.contributor.advisor Rossi, Sabina it_IT
dc.contributor.author Yoshida, Kotono <1995> it_IT
dc.date.accessioned 2019-06-20 it_IT
dc.date.accessioned 2019-11-20T07:10:10Z
dc.date.available 2019-11-20T07:10:10Z
dc.date.issued 2019-07-10 it_IT
dc.identifier.uri http://hdl.handle.net/10579/15499
dc.description.abstract In this thesis a security property for stochastic, cooperating processes expressed as terms of the Performance Evaluation Process Algebra (PEPA) is studied. It is expressed as the notion of Persistent Stochastic Non-Interference (PSNI). This work consists in the attempt of relaxing the strict condition of PSNI by introducing a novel equivalence relation over PEPA components, approximated strong equivalence, which induces a quasi-lumpable partition on the state-space of the underlying Markov process. Lumpability approach is a method to tackle the state space explosion problem by reducing the state space of a Markov chain. Equivalent states are aggregated into an unique partition, creating a new aggregated Markov chain that is smaller but its behaviour is the same as the original chain. However, the conditions for a partition on the original state space to be lumpable are quite strict. The introduction of quasi-lumpability is then an attempt to relax the conditions in order to aggregate the states of the considered Markov chain. In line with this thinking, also the property PSNI can be, in some sense, relaxed by adopting the approximated form of the concept of strong equivalence. For this purpose, this thesis can be divided in two main sections: one is the study about the concept of quasi-lumpability, the other is the application of quasi-lumpability to the property PSNI. it_IT
dc.language.iso en it_IT
dc.publisher Università Ca' Foscari Venezia it_IT
dc.rights © Kotono Yoshida, 2019 it_IT
dc.title Approximate Persistent Stochastic Non Interference it_IT
dc.title.alternative Approximate Persistent Stochastic Non-Interference it_IT
dc.type Bachelor Thesis it_IT
dc.degree.name Informatica - computer science it_IT
dc.degree.level Laurea magistrale it_IT
dc.degree.grantor Dipartimento di Scienze Ambientali, Informatica e Statistica it_IT
dc.description.academicyear 2018/2019_sessione_estiva it_IT
dc.rights.accessrights openAccess it_IT
dc.thesis.matricno 853696 it_IT
dc.subject.miur INF/01 INFORMATICA it_IT
dc.description.note it_IT
dc.degree.discipline it_IT
dc.contributor.co-advisor it_IT
dc.date.embargoend it_IT
dc.provenance.upload Kotono Yoshida (853696@stud.unive.it), 2019-06-20 it_IT
dc.provenance.plagiarycheck Sabina Rossi (rossisab@unive.it), 2019-07-08 it_IT


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