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
On the Effects of Data Protection on 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
2022 (English)In: Integrated Uncertainty in Knowledge Modelling and Decision Making: 9th International Symposium, IUKM 2022, Ishikawa, Japan, March 18–19, 2022, Proceedings / [ed] Katsuhiro Honda; Tomoe Entani; Seiki Ubukata; Van-Nam Huynh; Masahiro Inuiguchi, Springer, 2022, p. 226-238Conference paper, Published paper (Refereed)
Abstract [en]

This paper analyses the effects of masking mechanism for privacy preservation in data-driven models (regression) with respect to database integration. Especially two data masking methods (microaggregation and rank swapping) are applied on two public datasets to evaluate the linear regression model in terms of privacy protection and prediction performance. Our preliminary experimental results show that both methods achieve a good trade-off of privacy protection and information loss. We also show that for some experiments although data integration produces some incorrect links, the linear regression model is still comparable, with respect to prediction error, to the one inferred from the original data.

Place, publisher, year, edition, pages
Springer, 2022. p. 226-238
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 13199
Keywords [en]
Data protection, Masking methods, Microaggregation, Multidatabase integration, Rank swapping, Reidentification
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:umu:diva-193356DOI: 10.1007/978-3-030-98018-4_19ISI: 000786448900019Scopus ID: 2-s2.0-85126526408ISBN: 978-3-030-98017-7 (print)ISBN: 978-3-030-98018-4 (electronic)OAI: oai:DiVA.org:umu-193356DiVA, id: diva2:1648977
Conference
9th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2022
Note

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 13199).

Available from: 2022-04-01 Created: 2022-04-01 Last updated: 2023-09-05Bibliographically approved

Open Access in DiVA

fulltext(440 kB)110 downloads
File information
File name FULLTEXT01.pdfFile size 440 kBChecksum SHA-512
b264bda4761a782a2d5f1afe8c4c9213c87b40ae42c3815a29c1942c579d5275020e27f8ade0edc9027a3598dbd81450c32ade795668edf1188fe04dbc4678d3
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
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 111 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: 258 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