Improving microaggregation for complex record anonymization
Document typeConference report
Rights accessRestricted access - publisher's policy
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 − κ). 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.
CitationPont, J. [et al.]. Improving microaggregation for complex record anonymization. A: International Conference on Modeling Decisions for Artificial Intelligence. "The 5th International Conference on Modeling Decisions for Artificial Intelligence". Sabadell: Springer Verlag, 2008, p. 215-226.
- DMAG - Grup d'Aplicacions Multimèdia Distribuïdes - Ponències/Comunicacions de congressos 
- DAMA-UPC - Data Management Group de la Universitat Politècnica de Catalunya - Ponències/Comunicacions de congressos 
- Departament d'Arquitectura de Computadors - Ponències/Comunicacions de congressos 
- Departament de Teoria del Senyal i Comunicacions - Ponències/Comunicacions de congressos