Finding Curvilinear Features in Spatial Point Patterns: Principal Curve Clustering with Noise
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sensitive Micro Data Protection Using Latin Hypercube Sampling Technique
Inference Control in Statistical Databases, From Theory to Practice
Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation
Data Mining and Knowledge Discovery
Generating Sufficiency-based Non-Synthetic Perturbed Data
Transactions on Data Privacy
Statistical Disclosure Control for Microdata Using the R-Package sdcMicro
Transactions on Data Privacy
Random Forests for Generating Partially Synthetic, Categorical Data
Transactions on Data Privacy
Hybrid microdata using microaggregation
Information Sciences: an International Journal
Combinations of SDC methods for microdata protection
PSD'06 Proceedings of the 2006 CENEX-SDC project international conference on Privacy in Statistical Databases
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In this paper we propose a new scheme for statistical disclosure limitation which can be classified as a hybrid method of protection, that is, a method that combines properties of perturbative and synthetic methods. This approach is based on model-based clustering with the subsequent synthesis of the records within each cluster. The novelty is that the clustering and synthesis methods have been carefully chosen to fit each other in view of reducing information loss. The model-based clustering tries to obtain clusters such that the within-cluster data distribution is approximately normal; then we can use a multivariate normal synthesizer for the local synthesis of data. In this way, some of the non-normal characteristics of the data are captured by the clustering, so that a simple synthesizer for normal data can be used within each cluster. Our method is shown to be effective when compared to other disclosure limitation strategies.