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Preserving empirical data utility in k-anonymous microaggregation via linear discriminant analysis

Author
Rodríguez-Hoyos, A.; Rebollo-Monedero, D.; Estrada, J.; Forne, J.; Urquiza, L.
Type of activity
Journal article
Journal
Engineering applications of artificial intelligence
Date of publication
2020-09-01
Volume
94
First page
103787:1
Last page
103787:13
DOI
10.1016/j.engappai.2020.103787
Project funding
Secure SMArt Grid using Open Source Intelligence. Data Privacy and Reliable Communications
Repository
http://hdl.handle.net/2117/330076 Open in new window
URL
https://www.sciencedirect.com/science/article/abs/pii/S0952197620301792 Open in new window
Abstract
Today’s countless benefits of exploiting data come with a hefty price in terms of privacy. -Anonymous microaggregation is a powerful technique devoted to revealing useful demographic information of microgroups of people, whilst protecting the privacy of individuals therein. Evidently, the inherent distortion of data results in the degradation of its utility. This work proposes and analyzes an anonymization method that draws upon the technique of linear discriminant analysis (LDA), with the aim...
Citation
Rodríguez-Hoyos, A. [et al.]. Preserving empirical data utility in k-anonymous microaggregation via linear discriminant analysis. "Engineering applications of artificial intelligence", 1 Setembre 2020, vol. 94, p. 103787:1-103787:13.
Keywords
Data privacy, Data utility, LDA, Microaggregation, Statistical disclosure control
Group of research
SISCOM - Smart Services for Information Systems and Communication Networks

Participants

  • Rodríguez Hoyos, Ana Fernanda  (author)
  • Rebollo Monedero, David  (author)
  • Estrada Jimenez, Jose Antonio  (author)
  • Forné, Jordi  (author)
  • Urquiza Aguiar, Luis Felipe  (author)