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On reconstructability of quadratic utility functions from the iterations in gradient methods
KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre. KTH, Skolan för elektro- och systemteknik (EES), Reglerteknik.
2016 (engelsk)Inngår i: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 66, s. 254-261Artikkel i tidsskrift (Fagfellevurdert) Published
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Abstract [en]

In this paper, we consider a scenario where an eavesdropper can read the content of messages transmitted over a network. The nodes in the network are running a gradient algorithm to optimize a quadratic utility function where such a utility optimization is a part of a decision making process by an administrator. We are interested in understanding the conditions under which the eavesdropper can reconstruct the utility function or a scaled version of it and, as a result, gain insight into the decision-making process. We establish that if the parameter of the gradient algorithm, i.e., the step size, is chosen appropriately, the task of reconstruction becomes practically impossible for a class of Bayesian filters with uniform priors. We establish what step-size rules should be employed to ensure this. 

sted, utgiver, år, opplag, sider
Elsevier, 2016. Vol. 66, s. 254-261
Emneord [en]
Statistical inference, Data privacy, Gradient methods, Data confidentiality, Parameter identification, Quadratic programming
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URN: urn:nbn:se:kth:diva-184949DOI: 10.1016/j.automatica.2016.01.014ISI: 000371099300029Scopus ID: 2-s2.0-84959521193OAI: oai:DiVA.org:kth-184949DiVA, id: diva2:917819
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QC 20160407

Tilgjengelig fra: 2016-04-07 Laget: 2016-04-07 Sist oppdatert: 2017-11-30bibliografisk kontrollert

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