A General Additive Data Perturbation Method for Database Security
Management Science
Practical Data-Oriented Microaggregation for Statistical Disclosure Control
IEEE Transactions on Knowledge and Data Engineering
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Data ShufflingA New Masking Approach for Numerical Data
Management Science
Robust Statistics Meets SDC: New Disclosure Risk Measures for Continuous Microdata Masking
PSD '08 Proceedings of the UNESCO Chair in data privacy international conference on Privacy in Statistical Databases
Using the jackknife method to produce safe plots of microdata
PSD'06 Proceedings of the 2006 CENEX-SDC project international conference on Privacy in Statistical Databases
Software development for SDC in r
PSD'06 Proceedings of the 2006 CENEX-SDC project international conference on Privacy in Statistical Databases
Constrained Microaggregation: Adding Constraints for Data Editing
Transactions on Data Privacy
Hybrid microdata using microaggregation
Information Sciences: an International Journal
A modification of the Lloyd algorithm for k-anonymous quantization
Information Sciences: an International Journal
Testing of IHSN c++ code and inclusion of new methods into sdcmicro
PSD'12 Proceedings of the 2012 international conference on Privacy in Statistical Databases
Hybrid microdata via model-based clustering
PSD'12 Proceedings of the 2012 international conference on Privacy in Statistical Databases
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The demand for high quality microdata for analytical purposes has grown rapidly among researchers and the public over the last few years. In order to respect existing laws on data privacy and to be able to provide microdata to researchers and the public, statistical institutes, agencies and other institutions may provide masked data. Using our flexible software tools with which one can apply protection methods in an exploratory manner, it is possible to generate high quality confidential (micro-)data. In this paper we present highly flexible and easy to use software for the generation of anonymized microdata and give insights into the implementation and the design of the R-Package sdcMicro. R is a highly extendable system for statistical computing and graphics, distributed over the net. sdcMicro contains almost all popular methods for the anonymization of both categorical and continuous variables. Furthermore, several new methods have been implemented. The package can also be used for the comparison of methods and for measuring the information loss and disclosure risk of the masked data.