Trusted computation with an adversarial cloud

Shaunak D. Bopardikar, Alberto Speranzon, Cedric Langbort

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We consider the problem of computation in a cloud environment where either the data or the computation may be corrupted by an adversary. We assume that a small fraction of the data is stored locally at a client during the upload process to the cloud and that this data is trustworthy. We formulate the problem within a game theoretic framework where the client needs to decide an optimal fusion strategy using both non-trusted information from the cloud and local trusted data, given that the adversary on the cloud is trying to deceive the client by biasing the output to a different value/set of values. We adopt an Iterated Best Response (IBR) scheme for each player to update its action based on the opponent's announced computation. At each iteration, the cloud reveals its output to the client, who then computes the best response as a linear combination of its private local estimate and of the untrusted cloud output. We characterize equilibrium conditions for both the scalar and vector cases of the computed value of interest. Necessary and sufficient conditions for convergence for the IBR are derived and insightful geometric interpretations of such conditions is discussed for the vector case. Numerical results are presented showing the convergence conditions are relatively tight.

Original languageEnglish (US)
Title of host publicationACC 2015 - 2015 American Control Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2445-2452
Number of pages8
ISBN (Electronic)9781479986842
DOIs
StatePublished - Jul 28 2015
Event2015 American Control Conference, ACC 2015 - Chicago, United States
Duration: Jul 1 2015Jul 3 2015

Publication series

NameProceedings of the American Control Conference
Volume2015-July
ISSN (Print)0743-1619

Other

Other2015 American Control Conference, ACC 2015
CountryUnited States
CityChicago
Period7/1/157/3/15

Keywords

  • Adversarial Machine Learning
  • Equilibrium
  • Game theory
  • Trusted Computation

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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  • Cite this

    Bopardikar, S. D., Speranzon, A., & Langbort, C. (2015). Trusted computation with an adversarial cloud. In ACC 2015 - 2015 American Control Conference (pp. 2445-2452). [7171099] (Proceedings of the American Control Conference; Vol. 2015-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACC.2015.7171099