Umeå University's logo

umu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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 and multi-database data-driven models
Umeå University, Faculty of Science and Technology, Department of Computing Science.ORCID iD: 0000-0002-7788-3986
Umeå University, Faculty of Science and Technology, Department of Computing Science.ORCID iD: 0000-0002-0368-8037
2023 (English)In: Future Internet, E-ISSN 1999-5903, Vol. 15, no 3, article id 93Article in journal (Refereed) Published
Abstract [en]

Anonymization and data masking have effects on data-driven models. Different anonymization methods have been developed to provide a good trade-off between privacy guarantees and data utility. Nevertheless, the effects of data protection (e.g., data microaggregation and noise addition) on data integration and on data-driven models (e.g., machine learning models) built from these data are not known. In this paper, we study how data protection affects data integration, and the corresponding effects on the results of machine learning models built from the outcome of the data integration process. The experimental results show that the levels of protection that prevent proper database integration do not affect machine learning models that learn from the integrated database to the same degree. Concretely, our preliminary analysis and experiments show that data protection techniques have a lower level of impact on data integration than on machine learning models.

Place, publisher, year, edition, pages
MDPI, 2023. Vol. 15, no 3, article id 93
Keywords [en]
anonymization, data integration, data protection, masking
National Category
Computer Sciences Computer Systems
Identifiers
URN: urn:nbn:se:umu:diva-206361DOI: 10.3390/fi15030093ISI: 000956593800001Scopus ID: 2-s2.0-85150888833OAI: oai:DiVA.org:umu-206361DiVA, id: diva2:1753220
Available from: 2023-04-26 Created: 2023-04-26 Last updated: 2023-08-03Bibliographically approved

Open Access in DiVA

fulltext(565 kB)114 downloads
File information
File name FULLTEXT01.pdfFile size 565 kBChecksum SHA-512
5a5c20069207bc5c86f52af188415558015f3e5483a5e9426a5769098b88ab5c9a42457bc9f25b80522672719d0f38d77a906998dc42205aea4bf1e329b78094
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Authority records

Jiang, LiliTorra, Vicenç

Search in DiVA

By author/editor
Jiang, LiliTorra, Vicenç
By organisation
Department of Computing Science
In the same journal
Future Internet
Computer SciencesComputer Systems

Search outside of DiVA

GoogleGoogle Scholar
Total: 115 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
urn-nbn

Altmetric score

doi
urn-nbn
Total: 412 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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