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Incremental k-Anonymous microaggregation in large-scale electronic surveys with optimized scheduling

Author
Rebollo-Monedero, D.; Forne, J.; Soriano, M.; Hernández, C.
Type of activity
Journal article
Journal
IEEE access
Date of publication
2018-10-15
Volume
6
First page
60016
Last page
60044
DOI
https://doi.org/10.1109/ACCESS.2018.2875949 Open in new window
Project funding
Advanced forensics analysis
INRISCO. INcident monitoRing In Smart COmmunities .QoS and Privacy.
Microagregación Anónima en Encuestas Demográficas a Gran Escala
Repository
http://hdl.handle.net/2117/123435 Open in new window
URL
https://ieeexplore.ieee.org/document/8491270 Open in new window
Abstract
Improvements in technology have led to enormous volumes of detailed personal information made available for any number of statistical studies. This has stimulated the need for anonymization techniques striving to attain a difficult compromise between the usefulness of the data and the protection of our privacy. k-Anonymous microaggregation permits releasing a dataset where each person remains indistinguishable from other k–1 individuals, through the aggregation of demographic attributes, other...
Citation
Rebollo-Monedero, D., Hernández-Baigorri, C., Forne, J., Soriano, M. Incremental k-Anonymous microaggregation in large-scale electronic surveys with optimized scheduling. "IEEE access", 15 Octubre 2018, vol. 6, p. 60016-60044.
Keywords
Data privacy, Electronic surveys, K-anonymity, Large-scale datasets, Microaggregation, Statistical disclosure control
Group of research
ISG - Information Security Group
SISCOM - Smart Services for Information Systems and Communication Networks

Participants

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