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dc.contributor.advisorNascimento, Anderson C.A.
dc.contributor.authorMcKinney, Nicholas
dc.date.accessioned2018-04-24T22:16:44Z
dc.date.available2018-04-24T22:16:44Z
dc.date.submitted2018
dc.identifier.otherMcKinney_washington_0250O_18351.pdf
dc.identifier.urihttp://hdl.handle.net/1773/41726
dc.descriptionThesis (Master's)--University of Washington, 2018
dc.description.abstractIn this thesis, we implement a framework for secure multiparty computation. Our framework works in the commodity-based model, where the players running the distributed computation receive pre-distributed data from a trusted source during a setup phase. The framework allows secure multiplications of field elements, secure multiplications of matrices and matrix inversions. Differently from previous proposals, in our framework, the running times do not increase significantly as more players are added to the protocol. We illustrate the power of our solution by applying it to the problem of privately esti- mating driver’s drowsiness based on EEG data. We show that our private solution is practical and achieves a similar accuracy as a solution in the clear, where there are no privacy concerns.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.rightsnone
dc.subject
dc.subjectComputer science
dc.subject.otherTo Be Assigned
dc.titleSMPCEngine: An N-Party Implementation of the Secure Multiparty Private Computation Protocol
dc.typeThesis
dc.embargo.termsOpen Access


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