Towards the adaptation of SDC methods to stream mining

David Martínez Rodríguez, Jordi Nin, Miguel Nuñez-del-Prado

Producción científica: Contribución a una revistaArtículo de revista revisión exhaustiva

10 Citas (Scopus)


Most of the existing statistical disclosure control (SDC) standards, such as k-anonymity or l-diversity, were initially designed for static data. Therefore, they cannot be directly applied to stream data which is continuous, transient, and usually unbounded. Moreover, in streaming applications, there is a need to offer strong guarantees on the maximum allowed delay between incoming data and its corresponding anonymous output. In order to full-fill with these requirements, in this paper, we present a set of modifications to the most standard SDC methods, efficiently implemented within the Massive Online Analysis (MOA) stream mining framework. Besides, we have also developed a set of performance metrics to evaluate Information Loss and Disclosure Risk values continuously. Finally, we also show the efficiency of our new methods with a large set of experiments.
Idioma originalInglés
Páginas (desde-hasta)702-722
Número de páginas21
PublicaciónComputers and Security
EstadoPublicada - 1 set. 2017

Palabras clave

  • MOA Framework
  • Privacy
  • Statistical disclosure control
  • Stream mining
  • Stream processing


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