Improving microaggregation for complex record anonymization

Jordi Pont-Tuset, Jordi Nin, Pau Medrano-Gracia, Josep Ll Larriba-Pey, Victor Muntés-Mulero

Producció científica: Capítol de llibreContribució a congrés/conferènciaAvaluat per experts


Microaggregation is one of the most commonly employed microdata protection methods. This method builds clusters of at least k original records and replaces the records in each cluster with the centroid of the cluster. Usually, when records are complex, i.e., the number of attributes of the data set is large, this data set is split into smaller blocks of attributes and microaggregation is applied to each block, successively and independently. In this way, the information loss when collapsing several values to the centroid of their group is reduced, at the cost of losing the k-anonymity property when at least two attributes of different blocks are known by the intruder. In this work, we present a new microaggregation method called One dimension microaggregation (Mic1D - k). This method gathers all the values of the data set into a single sorted vector, independently of the attribute they belong to. Then, it microaggregates all the mixed values together. Our experiments show that, using real data, our proposal obtains lower disclosure risk than previous approaches whereas the information loss is preserved.

Idioma originalAnglès
Títol de la publicacióModeling Decisions for Artificial Intelligence - 5th International Conference, MDAI 2008, Proceedings
EditorSpringer Verlag
Nombre de pàgines12
ISBN (imprès)3540882685, 9783540882688
Estat de la publicacióPublicada - 2008
Publicat externament
Esdeveniment5th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2008 - Sabadell, Spain
Durada: 30 d’oct. 200831 d’oct. 2008

Sèrie de publicacions

NomLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volum5285 LNAI
ISSN (imprès)0302-9743
ISSN (electrònic)1611-3349


Conferència5th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2008


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