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Efficient k-anonymous microaggregation of multivariate numerical data via principal component analysis

Author
Rebollo-Monedero, D.; Mezher, A.; Casanova, X.; Forne, J.; Soriano, M.
Type of activity
Journal article
Journal
Information sciences
Date of publication
2019-07-09
Volume
503
First page
417
Last page
443
DOI
10.1016/j.ins.2019.07.042
Project funding
Enhancing critical infraestructure protection with innovative security framework
INRISCO. INcident monitoRing In Smart COmmunities .QoS and Privacy.
Secure SMArt Grid using Open Source Intelligence. Data Privacy and Reliable Communications
Repository
http://hdl.handle.net/2117/166168 Open in new window
URL
https://www.sciencedirect.com/science/article/pii/S0020025519306474 Open in new window
Abstract
k-Anonymous microaggregation is a widespread technique to address the problem of protecting the privacy of the respondents involved beyond the mere suppression of their identifiers, in applications where preserving the utility of the information disclosed is critical. Unfortunately, microaggregation methods with high data utility may impose stringent computational demands when dealing with datasets containing a large number of records and attributes. This work proposes and analyzes various anony...
Citation
Rebollo-Monedero, D. [et al.]. Efficient k-anonymous microaggregation of multivariate numerical data via principal component analysis. "Information sciences", 9 Juliol 2019, vol. 503, p. 417-443.
Keywords
Data privacy, K-anonymity, Large-scale datasets, Microaggregation, Principal component analysis, Statistical disclosure control
Group of research
ISG - Information Security Group
SISCOM - Smart Services for Information Systems and Communication Networks

Participants