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The Fast Maximum Distance to Average Vector (F-MDAV): an algorithm for k-Anonymous microaggregation in big data

Author
Rodríguez-Hoyos, A.; Estrada, J.; Rebollo-Monedero, D.; Mezher, A.; Parra-Arnau, J.; Forne, J.
Type of activity
Journal article
Journal
Engineering applications of artificial intelligence
Date of publication
2020-02-10
Volume
90
Number
April 2020
First page
103531:1
Last page
103531:12
DOI
10.1016/j.engappai.2020.103531
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/2117/178139 Open in new window
URL
https://www.sciencedirect.com/science/article/abs/pii/S095219762030035X Open in new window
Abstract
The massive exploitation of tons of data is currently guiding critical decisions in domains such as economics or health. But serious privacy risks arise since personal data is commonly involved. k-Anonymous microaggregation is a well-known method that guarantees individuals’ privacy while preserving much of data utility. Unfortunately, methods like this are computationally expensive in big data settings, whereas the application domain of data might require an immediate response to make “life...
Citation
Rodríguez-Hoyos, A. [et al.]. The Fast Maximum Distance to Average Vector (F-MDAV): an algorithm for k-Anonymous microaggregation in big data. "Engineering applications of artificial intelligence", 10 Febrer 2020, vol. 90, núm. April 2020, p. 103531:1-103531:12.
Keywords
Big data, Data privacy, MDAV, Speed-up, k-anonymous microaggregation
Group of research
SISCOM - Smart Services for Information Systems and Communication Networks

Participants

  • Rodríguez Hoyos, Ana Fernanda  (author)
  • Estrada Jimenez, Jose Antonio  (author)
  • Rebollo-Monedero, David  (author)
  • Mezher, Ahmad  (author)
  • Parra Arnau, Javier  (author)
  • Forné, Jordi  (author)