Practical Data-Oriented Microaggregation for Statistical Disclosure Control
IEEE Transactions on Knowledge and Data Engineering
Microdata Protection through Noise Addition
Inference Control in Statistical Databases, From Theory to Practice
Sensitive Micro Data Protection Using Latin Hypercube Sampling Technique
Inference Control in Statistical Databases, From Theory to Practice
Model Based Disclosure Protection
Inference Control in Statistical Databases, From Theory to Practice
Maximum entropy simulation for microdata protection
Statistics and Computing
Information preserving statistical obfuscation
Statistics and Computing
A rejoinder to the comments by Polettini and Stander
Statistics and Computing
Source Data Perturbation and consistent sets of safe tables
Statistics and Computing
Preserving confidentiality of high-dimensional tabulated data: Statistical and computational issues
Statistics and Computing
Model Diagnostics for Remote Access Regression Servers
Statistics and Computing
Remote access systems for statistical analysis of microdata
Statistics and Computing
Disclosure risk assessment in statistical microdata protection via advanced record linkage
Statistics and Computing
Maximum entropy simulation for microdata protection
Statistics and Computing
A theoretical basis for perturbation methods
Statistics and Computing
A rejoinder to the comments by Polettini and Stander
Statistics and Computing
Model Diagnostics for Remote Access Regression Servers
Statistics and Computing
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In this paper we discuss methodology for the safe release of business microdata. In particular we extend the model-based protection procedure of Franconi and Stander (2002, The Statistician 51: 1–11) by allowing the model to take account of the spatial structure underlying the geographical information in the microdata. We discuss the use of the Gibbs sampler for performing the computations required by this spatial approach. We provide an empirical comparison of these non-spatial and spatial disclosure limitation methods based on the Italian sample from the Community Innovation Survey. We quantify the level of protection achieved for the released microdata and the error induced when various inferences are performed. We find that although the spatial method often induces higher inferential errors, it almost always provides more protection. Moreover the aggregated areas from the spatial procedure can be somewhat more spatially smooth, and hence possibly more meaningful, than those from the non-spatial approach. We discuss possible applications of these model-based protection procedures to more spatially extensive data sets.