Revisiting online anonymization algorithms to ensure location privacy

Miguel Nunez-del-Prado, Jordi Nin

Producción científica: Contribución a una revistaArtículo de revista revisión exhaustiva

3 Citas (Scopus)


Individuals are continually observed and monitored by many location-based services, such as social networks, telecommunication companies, mobile networks, etc. The resulting streams of data, which are usually analyzed in real time, can reveal sensitive information about individuals, e.g. home/work location or private mobility patterns. Therefore, there is a need for stream processing algorithms able to anonymize datasets in real time to ensure certain privacy guarantees, but at the same time keeping a low error. In this paper, we describe how statistical disclosure control (SDC) methods can be applied to a Call Detail Record (CDR) database in a stream fashion to mask location information efficiently. Besides, we also provide some experimental results over a real database.
Idioma originalInglés
Páginas (desde-hasta)15097-15108
Número de páginas12
PublicaciónJournal of Ambient Intelligence and Humanized Computing
Fecha en línea anticipada3 jul. 2019
EstadoPublicada - nov. 2023

Nota bibliográfica

Publisher Copyright:
© 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

Palabras clave

  • CDR privacy
  • Location privacy
  • Online privacy
  • Stream anonymization


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