Parallelizing record linkage for disclosure risk assessment

Joan Guisado-Gámez, Arnau Prat-Pérez, Jordi Nin, Victor Muntés-Mulero, Josep Ll Larriba-Pey

Producción científica: Capítulo del libroContribución a congreso/conferenciarevisión exhaustiva

4 Citas (Scopus)


Handling very large volumes of confidential data is becoming a common practice in many organizations such as statistical agencies. This calls for the use of protection methods that have to be validated in terms of the quality they provide. With the use of Record Linkage (RL) it is possible to compute the disclosure risk, which gives a measure of the quality of a data protection method. However, the RL methods proposed in the literature are computationally costly, which poses difficulties when frequent RL processes have to be executed on large data. Here, we propose a distributed computing technique to improve the performance of a RL process. We show that our technique not only improves the computing time of a RL process significantly, but it is also scalable in a distributed environment. Also, we show that distributed computation can be complemented with SMP based parallelization in each node increasing the final speedup.

Idioma originalInglés
Título de la publicación alojadaPrivacy in Statistical Databases - UNESCO Chair in Data Privacy International Conference, PSD 2008, Proceedings
EditorialSpringer Verlag
Número de páginas13
ISBN (versión impresa)3540874704, 9783540874706
EstadoPublicada - 2008
Publicado de forma externa
EventoInternational Conference on Privacy in Statistical Databases, PSD 2008 - Istanbul, Turquía
Duración: 24 sept 200826 sept 2008

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen5262 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349


ConferenciaInternational Conference on Privacy in Statistical Databases, PSD 2008


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