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
Data Protection for Online Social Networks and P-Stability for Graphs
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0002-0368-8037
University of Konstanz, Konstanz, Germany.
Artificial Intelligence Research Institute(IIIA)–Spanish National Research Council (CSIC), Bellaterra 08193, Catalonia, Spain.
2016 (English)In: IEEE Transactions on Emerging Topics in Computing, ISSN 2168-6750, Vol. 4, no 3, p. 374-381Article in journal (Refereed) Published
Abstract [en]

Graphs can be used as a model for online social networks. In this framework, vertices represent individuals and edges relationships between individuals. In recent years, different approaches have been considered to offer data privacy to online social networks and for developing graph protection. Perturbative approaches are formally defined in terms of perturbation and modification of graphs. In this paper, we discuss the concept of P-stability on graphs and its relation to data privacy. The concept of P-stability is rooted in the number of graphs given a fixed degree sequence. In this paper, we show that for any graph there exists a class of P-stable graphs. This result implies that there is a fully polynomial randomized approximation for graph masking for the graphs in the class. In order to further refine the classification of a given graph, we introduce the concept of natural class of a graph. It is based on a class of scale-free networks.

Place, publisher, year, edition, pages
IEEE, 2016. Vol. 4, no 3, p. 374-381
Keywords [en]
Graphs, P-stability, data privacy
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
URN: urn:nbn:se:his:diva-13313DOI: 10.1109/TETC.2015.2433923ISI: 000390197500006Scopus ID: 2-s2.0-84986536260OAI: oai:DiVA.org:his-13313DiVA, id: diva2:1065057
Available from: 2017-01-13 Created: 2017-01-13 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
IEEE Transactions on Emerging Topics in Computing
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 403 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