Practical Data-Oriented Microaggregation for Statistical Disclosure Control

By Josep Domingo-Ferrer, Josep Maria Mateo-Sanz. IEEE Transactions on Knowledge and Data Engineering, 2002

684 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/9796741f06dc309163ba99993d9ef31295ec86a8

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Citations in the corpus

A Survey of Inference Control Methods for Privacy-Preserving Data Mining (2008, methodology): “Finally, F ′ is sampled to obtain a synthetic microdata set Z .”
A survey on privacy in mobile participatory sensing applications (2011, background): “In contrast, microaggregation [79] does not generalize the participant location to a cell, but replaces the real location by the averaged location of the k nearest participants, the so called equivalence class.”
Privacy and Data Protection by Design - from policy to engineering (2014, background): “Microaggregation [87] partitions records in a data set into groups containing each at least k records; then the average record of each group is published.”
Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation (2005, methodology): “Microaggregation is a family of perturbative SDC methods originally defined for continuous data (Defays and Nanopoulos, 1993; Domingo-Ferrer and Mateo-Sanz, 2002) and recently extended for categorical data (Torra, 2004).”
Risk‐Utility Paradigms for Statistical Disclosure Limitation: How to Think, But Not How to Act (2011, background): “Instead, we consider a novel variant of multivariate microaggregation (Domingo-Ferrer and Mateo-Sanz, 2002; Domingo-Ferrer et al., 2006), which we term indexed microaggregation.”
A General Survey of Privacy-Preserving Data Mining Models and Algorithms (2008, background): “Since each cluster contains at least k-records, the anonymized data is guaranteed to at least satisfy the definitions of k-anonymity.”
Knowledge-based Systems (2009, neutral): “The best-performing approach is the sequential combination of three proposed methods ( 𝑀𝑂 , 𝑀𝐶 , and 𝑇𝐵 ): V a l u e s”
Composition attacks and auxiliary information in data privacy (2008, methodology): “For example, microaggregation [11] clusters individuals based on Euclidean distance.”