Interactive Anonymization for Privacy aware Machine Learning

Bernd Malle, Peter Kieseberg, Andreas Holzinger

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


Privacy aware Machine Learning is the discipline of applying
Machine Learning techniques in such a way as to protect and retain personal identities during the process. This is most easily achieved by first
anonymizing a dataset before releasing it for the purpose of data mining or knowledge extraction. Starting in June 2018, this will also remain
the sole legally permitted way within the EU to release data without
granting people involved the right to be forgotten, i.e. the right to have
their data deleted on request. To governments, organizations and corporations, this represents a serious impediment to research operations,
since any anonymization results in a certain degree of reduced data utility. In this paper we propose applying human background knowledge via
interactive Machine Learning to the process of anonymization; this is
done by eliciting human preferences for preserving some attribute values
over others in the light of specific tasks. Our experiments show that human knowledge can yield measurably better classification results than a
rigid automatic approach. However, the impact of interactive learning in
the field of anonymization will largely depend on the experimental setup,
such as an appropriate choice of application domain as well as suitable
test subjects.
Original languageEnglish
Title of host publicationCEUR Workshop Proceedings
Pages15 - 26
Publication statusPublished - 2017
Event2017 Workshop and Tutorial on Interactive Adaptive Learning: IAL 2017 - Skopje, North Macedonia
Duration: 18 Sept 2017 → …


Conference2017 Workshop and Tutorial on Interactive Adaptive Learning
Country/TerritoryNorth Macedonia
Period18/09/17 → …


  • privacy aware machine learning

ASJC Scopus subject areas

  • Artificial Intelligence

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