Publication:

A clustering approach to anonymize locations during dataset de-identification

Date

2021
Proceedings Paper
https://doi.org/10.1145/3465481.3470020
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dc.contributor.authorVerdonck, Jenno
dc.contributor.authorDe Boeck, Kevin
dc.contributor.authorWillocx, Michiel
dc.contributor.authorLapon, Jorn
dc.contributor.authorNaessens, Vincent
dc.contributor.orcidextVerdonck, Jenno::0000-0003-3448-1554
dc.contributor.orcidextDe Boeck, Kevin::0000-0002-7143-8742
dc.contributor.orcidextWillocx, Michiel::0000-0003-0225-9705
dc.date.accessioned2023-07-11T09:47:06Z
dc.date.available2023-06-20T10:35:58Z
dc.date.available2023-07-11T09:47:06Z
dc.date.embargo2021-12-31
dc.date.issued2021
dc.identifier.doi10.1145/3465481.3470020
dc.identifier.eisbn978-1-4503-9051-4
dc.identifier.issnna
dc.identifier.urihttps://imec-publications.be/handle/20.500.12860/41923
dc.publisherASSOC COMPUTING MACHINERY
dc.source.beginpageArt. 97
dc.source.conference16th International Conference on Availability, Reliability and Security (ARES)
dc.source.conferencedateAUG 17-20, 2021
dc.source.conferencelocationVienna
dc.source.journalna
dc.source.numberofpages10
dc.subject.keywordsPRIVACY
dc.title

A clustering approach to anonymize locations during dataset de-identification

dc.typeProceedings paper
dspace.entity.typePublication
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