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Fuzzy clustering-based microaggregation to achieve probabilistic k-anonymity for data with constraints
University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. Hamilton Institute, Maynooth University, Ireland. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0002-0368-8037
2020 (English)In: Journal of Intelligent & Fuzzy Systems, ISSN 1064-1246, E-ISSN 1875-8967, Vol. 39, no 5, p. 5999-6008Article in journal (Refereed) Published
Abstract [en]

Microaggregation is an effective data-driven protection method that permits us to achieve a good trade-off between disclosure risk and information loss. In this work we propose a method for microaggregation based on fuzzy c-means, that is appropriate when there are constraints (linear constraints) on the variables that describe the data. Our method leads to results that satisfy these constraints even when the data to be masked do not satisfy them. 

Place, publisher, year, edition, pages
IOS Press, 2020. Vol. 39, no 5, p. 5999-6008
Keywords [en]
clustering statistical disclosure control, data privacy, edit constraints, k-Anonymity, Microaggregation, Economic and social effects, Data driven, Disclosure risk, Fuzzy C mean, Information loss, Linear constraints, Protection methods, Fuzzy clustering
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
URN: urn:nbn:se:his:diva-19308DOI: 10.3233/JIFS-189074ISI: 000595520600004Scopus ID: 2-s2.0-85096990170OAI: oai:DiVA.org:his-19308DiVA, id: diva2:1508850
Part of project
Disclosure risk and transparency in big data privacy, Swedish Research Council
Funder
Swedish Research Council, 2016-03346
Note

CC BY-NC 4.0

Partial support of the project Swedish Research Council (Vetenskapsrådet) (grant number VR 2016-03346) is acknowledged.

DRIAT

Available from: 2020-12-10 Created: 2020-12-10 Last updated: 2021-08-18Bibliographically approved

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Torra, Vicenç

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