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
Explaining Recurrent Machine Learning Models: Integral Privacy Revisited
University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. Department of Computing Science, Umeå University, Sweden. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0002-0368-8037
Department Information and Communications Engineering – CYBERCAT, Universitat Autònoma de Barcelona, Bellaterra, Catalonia, Spain.
Naturally Inspired Computation Research Group, Department of Computer Science, Maynooth University, Ireland.
2020 (English)In: Privacy in Statistical Databases: UNESCO Chair in Data Privacy, International Conference, PSD 2020, Tarragona, Spain, September 23–25, 2020, Proceedings / [ed] Josep Domingo-Ferrer, Krishnamurty Muralidhar, Cham: Springer, 2020, p. 62-73Conference paper, Published paper (Refereed)
Abstract [en]

We have recently introduced a privacy model for statistical and machine learning models called integral privacy. A model extracted from a database or, in general, the output of a function satisfies integral privacy when the number of generators of this model is sufficiently large and diverse. In this paper we show how the maximal c-consensus meets problem can be used to study the databases that generate an integrally private solution. We also introduce a definition of integral privacy based on minimal sets in terms of this maximal c-consensus meets problem. 

Place, publisher, year, edition, pages
Cham: Springer, 2020. p. 62-73
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 12276
Keywords [en]
Clustering, Integral privacy, Maximal c-consensus meets, Parameter selection, Data privacy, Database systems, Machine learning models, Privacy models, Machine learning
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
URN: urn:nbn:se:his:diva-19186DOI: 10.1007/978-3-030-57521-2_5Scopus ID: 2-s2.0-85092091090ISBN: 978-3-030-57520-5 (print)ISBN: 978-3-030-57521-2 (electronic)OAI: oai:DiVA.org:his-19186DiVA, id: diva2:1476762
Conference
UNESCO Chair in Data Privacy, International Conference, PSD 2020, Tarragona, Spain, September 23–25, 2020
Part of project
Disclosure risk and transparency in big data privacy, Swedish Research Council
Funder
Swedish Research Council, 2016-03346
Note

CC BY 4.0

Also part of the Information Systems and Applications, incl. Internet/Web, and HCI book sub series (LNISA, volume 12276)

Partial support of the project Swedish Research Council (grant number VR 2016-03346) is acknowledged.

DRIAT

Available from: 2020-10-15 Created: 2020-10-15 Last updated: 2021-08-18Bibliographically approved

Open Access in DiVA

fulltext(308 kB)208 downloads
File information
File name FULLTEXT01.pdfFile size 308 kBChecksum SHA-512
6a3288fdb4bab92d75f047bb4e21ca2e08b9fc1009f74d5de8db489eb9c34ee1ee96915dad2d27c056fb5269ebbae968de99e053fa782a552721075598c71904
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Authority records

Torra, Vicenç

Search in DiVA

By author/editor
Torra, Vicenç
By organisation
School of InformaticsInformatics Research Environment
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 208 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 195 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