Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • apa.csl
  • 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
Privacy-Enhancing Technologies and Anonymisation in Light of GDPR and Machine Learning
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).ORCID iD: 0000-0002-6938-4466
Unabhängiges Landeszentrum für Datenschutz Schleswig-Holstein, Germany.
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). Radboud University, Netherlands;University of Groningen, Netherlands.
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
2023 (English)In: Privacy and Identity Management / [ed] Felix Bieker, Joachim Meyer, Sebastian Pape, Ina Schiering, Andreas Weich, Springer, 2023, Vol. 671 IFIP, p. 11-20Conference paper, Published paper (Refereed)
Abstract [en]

The use of Privacy-Enhancing Technologies in the field of data anonymisation and pseudonymisation raises a lot of questions with respect to legal compliance under GDPR and current international data protection legislation. Here, especially the use of innovative technologies based on machine learning may increase or decrease risks to data protection. A workshop held at the IFIP Summer School on Privacy and Identity Management showed the complexity of this field and the need for further interdisciplinary research on the basis of an improved joint understanding of legal and technical concepts. 

Place, publisher, year, edition, pages
Springer, 2023. Vol. 671 IFIP, p. 11-20
Series
IFIP Advances in Information and Communication Technology, ISSN 1868-4238, E-ISSN 1868-422X
Keywords [en]
Machine learning, Anonymization, Data anonymization, Innovative technology, Legal compliance, Machine-learning, On-machines, Privacy enhancing technologies, Summer school, Technology-based, Data privacy
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-97456DOI: 10.1007/978-3-031-31971-6_2Scopus ID: 2-s2.0-85173556437OAI: oai:DiVA.org:kau-97456DiVA, id: diva2:1813896
Conference
IFIP International Summer School on Privacy and Identity Management,[Digital], August 30-September 2, 2022.
Available from: 2023-11-22 Created: 2023-11-22 Last updated: 2023-11-22Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Fischer-Hübner, SimoneHoepman, Jaap-HenkJensen, Meiko

Search in DiVA

By author/editor
Fischer-Hübner, SimoneHoepman, Jaap-HenkJensen, Meiko
By organisation
Department of Mathematics and Computer Science (from 2013)
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 133 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • apa.csl
  • 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