his.sePublikasjoner
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Graphic sequences, distances and k-degree anonymity
IIIA-CSIC, Consejo Superior de Investigaciones Científicas, Institut d’Investigació en Intelligència Artificial, Campus Universitat Autònoma de Barcelona, Spain.
Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. IIIA-CSIC, Consejo Superior de Investigaciones Científicas, Institut d’Investigació en Intelligència Artificial, Campus Universitat Autònoma de Barcelona, Spain. (Skövde Artificial Intelligence Lab (SAIL))ORCID-id: 0000-0002-0368-8037
2015 (engelsk)Inngår i: Discrete Applied Mathematics, ISSN 0166-218X, E-ISSN 1872-6771, Vol. 188, nr 1, s. 25-31Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

In this paper we study conditions to approximate a given graph by a regular one. We obtain optimal conditions for a few metrics such as the edge rotation distance for graphs, the rectilinear and the Euclidean distance over degree sequences. Then, we require the approximation to have at least kk copies of each value in the degree sequence, this is a property proceeding from data privacy that is called kk-degree anonymity.

We give a sufficient condition in order for a degree sequence to be graphic that depends only on its length and its maximum and minimum degrees. Using this condition we give an optimal solution of kk-degree anonymity for the Euclidean distance when the sum of the degrees in the anonymized degree sequence is even. We present algorithms that may be used for obtaining all the mentioned anonymizations.

sted, utgiver, år, opplag, sider
Elsevier, 2015. Vol. 188, nr 1, s. 25-31
HSV kategori
Forskningsprogram
Teknik; Skövde Artificial Intelligence Lab (SAIL)
Identifikatorer
URN: urn:nbn:se:his:diva-10938DOI: 10.1016/j.dam.2015.03.005ISI: 000355767900003Scopus ID: 2-s2.0-84933279022OAI: oai:DiVA.org:his-10938DiVA, id: diva2:811982
Forskningsfinansiär
EU, FP7, Seventh Framework Programme, 262608Tilgjengelig fra: 2015-05-13 Laget: 2015-05-13 Sist oppdatert: 2018-03-28bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekstScopus

Personposter BETA

Torra, Vicenç

Søk i DiVA

Av forfatter/redaktør
Torra, Vicenç
Av organisasjonen
I samme tidsskrift
Discrete Applied Mathematics

Søk utenfor DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric

doi
urn-nbn
Totalt: 355 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf