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Mathematically optimized, recursive prepartitioning strategies for k-anonymous microaggregation of large-scale datasets

Author
Pallares, E.; Rebollo-Monedero, D.; Rodríguez-Hoyos, A.; Estrada, J.; Mezher, A.; Forne, J.
Type of activity
Journal article
Journal
Expert systems with applications
Date of publication
2019-11-11
Volume
144
First page
113086:1
Last page
113086:17
DOI
10.1016/j.eswa.2019.113086
Project funding
Anonymous microaggregation in large-scale demographic surveys
Secure SMArt Grid using Open Source Intelligence. Data Privacy and Reliable Communications
Repository
http://hdl.handle.net/2117/173796 Open in new window
URL
https://www.sciencedirect.com/science/article/pii/S0957417419308036 Open in new window
Abstract
The technical contents of this work fall within the statistical disclosure control (SDC) field, which concerns the postprocessing of the demographic portion of the statistical results of surveys containing sensitive personal information, in order to effectively safeguard the anonymity of the participating respondents. A widely known technique to solve the problem of protecting the privacy of the respondents involved beyond the mere suppression of their identifiers is the k-anonymous microaggrega...
Citation
Pallares, E. [et al.]. Mathematically optimized, recursive prepartitioning strategies for k-anonymous microaggregation of large-scale datasets. "Expert systems with applications", 11 Novembre 2019, vol. 144, p. 113086:1-113086:17.
Keywords
Data privacy, Large-scale datasets, Microaggregation, Optimized prepartitioning, Statistical disclosure control, k-anonymity
Group of research
SISCOM - Smart Services for Information Systems and Communication Networks

Participants