Högskolan i Skövde

his.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • apa-cv
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Improving the characterization of P-stability for applications in network privacy
Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, Tarragona, Spain.
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0002-0368-8037
2016 (English)In: Discrete Applied Mathematics, ISSN 0166-218X, E-ISSN 1872-6771, Vol. 206, p. 109-114Article in journal (Refereed) Published
Resource type
Text
Abstract [en]

Recently, we have found that the concept of P-stability has interesting applications in network privacy. In the context of Online Social Networks it may be used for obtaining a fully polynomial randomized approximation scheme for graph masking and measuring disclosure risk. Also by using the characterization for P-stable sequences from Jerrum, McKay and Sinclair (1992) it is possible to obtain optimal approximations for the problem of k-degree anonymity. In this paper, we present results on P-stability considering the additional restriction that the degree sequence must not intersect the edges of an excluded graph X, improving earlier results on P-stability. As a consequence we extend the P-stable classes of scale-free networks from Torra et al. (2015), obtain an optimal solution for k-anonymity and prove that all the known conditions for P-stability are sufficient for sequences to be graphic. (C) 2016 Elsevier B.V. All rights reserved.

Place, publisher, year, edition, pages
Elsevier, 2016. Vol. 206, p. 109-114
Keywords [en]
P-stability, k-anonymity, Graphic sequence, Degree sequence, FPRAS, Rapidly mixing Markov chain, Fully polynomial-time randomized approximation scheme
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
URN: urn:nbn:se:his:diva-12568DOI: 10.1016/j.dam.2016.01.025ISI: 000376542600012Scopus ID: 2-s2.0-84994589863OAI: oai:DiVA.org:his-12568DiVA, id: diva2:941565
Available from: 2016-06-22 Created: 2016-06-22 Last updated: 2018-03-28Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Torra, Vicenç

Search in DiVA

By author/editor
Torra, Vicenç
By organisation
School of InformaticsThe Informatics Research Centre
In the same journal
Discrete Applied Mathematics
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 551 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • apa-cv
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf