Algorithms for clustering data
Algorithms for clustering data
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Practical Data-Oriented Microaggregation for Statistical Disclosure Control
IEEE Transactions on Knowledge and Data Engineering
Disclosure Risk Assessment in Perturbative Microdata Protection
Inference Control in Statistical Databases, From Theory to Practice
Mondrian Multidimensional K-Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Rethinking rank swapping to decrease disclosure risk
Data & Knowledge Engineering
Privacy-Preserving Data Mining: Models and Algorithms
Privacy-Preserving Data Mining: Models and Algorithms
Hybrid microdata using microaggregation
Information Sciences: an International Journal
Privacy-Aware Knowledge Discovery: Novel Applications and New Techniques
Privacy-Aware Knowledge Discovery: Novel Applications and New Techniques
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The need of improving the privacy on public datasets is becoming more and more important because the number of public available datasets is growing very fast. This forced the continuous research to find better protection methods that prevent the disclosure of the entities or individuals in a dataset while preserving the data utility. In this paper we present a new approach for categorical data protection based on applying clustering to the dataset and then protecting each cluster. We show that this new approach allow us to have protections with better trade-off between data utility and individuals information disclosure.