A modified random perturbation method for database security
ACM Transactions on Database Systems (TODS)
On Finding the Maxima of a Set of Vectors
Journal of the ACM (JACM)
Microdata Protection through Noise Addition
Inference Control in Statistical Databases, From Theory to Practice
Disclosure Risk Assessment in Perturbative Microdata Protection
Inference Control in Statistical Databases, From Theory to Practice
On the security of microaggregation with individual ranking: analytical attacks
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Software systems for tabular data releases
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Verification servers: Enabling analysts to assess the quality of inferences from public use data
Computational Statistics & Data Analysis
Hybrid microdata using microaggregation
Information Sciences: an International Journal
Multiplicative noise protocols
PSD'10 Proceedings of the 2010 international conference on Privacy in statistical databases
Software—Practice & Experience - Focus on Selected PhD Literature Reviews in the Practical Aspects of Software Technology
Anonymization methods for taxonomic microdata
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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A number of methods have been proposed in the literature for masking (protecting) microdata. Nearly all of these methods may be implemented with different degrees of intensity, by setting the value of an appropriate parameter. However, even parameter variation may not be sufficient to realize appropriate levels of disclosure risk and data utility. In this paper we propose a new approach to protection of numerical microdata: applying multiple stages of masking to the data in a way that increases utility but controls disclosure risk.