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On the Privacy of Optimization
KTH, School of Electrical Engineering (EES), Network and Systems engineering.ORCID iD: 0000-0001-9810-3478
2017 (English)In: IFAC-PapersOnLine, ISSN 2405-8963, Vol. 50, no 1, p. 9502-9508Article in journal (Refereed) Published
Abstract [en]

In distributed or multiparty computations, optimization theory methods offer appealing privacy properties compared to cryptography and differential privacy methods. However, unlike cryptography and differential privacy, optimization methods currently lack a formal quantification of the privacy they can provide. The main contribution of this paper is to propose a quantification of the privacy of a broad class of optimization approaches. The optimization procedures generate a problem's data ambiguity for an adversarial observer, which thus observes the problem's data within an uncertainty set. We formally define a one-to-many relation between a given adversarial observed message and an uncertainty set of the problem's data. Based on the uncertainty set, a privacy measure is then formalized. The properties of the proposed privacy measure are analyzed. The key ideas are illustrated with examples, including localization and average consensus.

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 50, no 1, p. 9502-9508
Keywords [en]
ADMM, distributed optimization, Privacy, secured multiparty computation
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:kth:diva-223055DOI: 10.1016/j.ifacol.2017.08.1590ISI: 000423965100082Scopus ID: 2-s2.0-85031768514OAI: oai:DiVA.org:kth-223055DiVA, id: diva2:1182434
Note

QC 20180213

Available from: 2018-02-13 Created: 2018-02-13 Last updated: 2018-03-05Bibliographically approved

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Fischione, Carlo

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Citation style
  • apa
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  • de-DE
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  • nn-NB
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  • Other locale
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Output format
  • html
  • text
  • asciidoc
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