V-MDAV

V-MDAV: A multivariate microaggregation with variable group size. Microaggregation is a clustering problem with minimum size constraints on the resulting clusters or groups; the number of groups is unconstrained and the within-group homogeneity should be maximized. In the context of privacy in statistical databases, microaggregation is a well-known approach to obtain ing anonymized versions of confidential microdata. Optimally solving microaggregation on multivariate data sets is known to be difficult (NP-hard). Therefore, heuristic methods are used in practice. This paper presents a new heuristic approach to multivariate microaggregation, which provides variable-sized groups (and thus higher within-group homogeneity) with a computational cost similar to the one of fixed-size microaggregation heuristics.


References in zbMATH (referenced in 6 articles )

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  1. Monedero, David Rebollo; Mezher, Ahmad Mohamad; Colomé, Xavier Casanova; Forné, Jordi; Soriano, Miguel: Efficient (k)-anonymous microaggregation of multivariate numerical data via principal component analysis (2019)
  2. Casino, Fran; Domingo-Ferrer, Josep; Patsakis, Constantinos; Puig, Domènec; Solanas, Agusti: A (k)-anonymous approach to privacy preserving collaborative filtering (2015) ioport
  3. Aloise, Daniel; Hansen, Pierre; Rocha, Caroline; Santi, Éverton: Column generation bounds for numerical microaggregation (2014)
  4. Oommen, B. John; Fayyoumi, Ebaa: On utilizing dependence-based information to enhance micro-aggregation for secure statistical databases (2013) ioport
  5. Schneeweiss, Hans; Rost, Daniel; Schmid, Matthias: Probability and quantile estimation from individually micro-aggregated data (2012)
  6. Solanas, Agusti; Di Pietro, Roberto: A linear-time multivariate micro-aggregation for privacy protection in uniform very large data sets (2008)