Spatial and non-spatial model-based protection procedures for the release of business microdata
Statistics and Computing
Maximum entropy simulation for microdata protection
Statistics and Computing
A theoretical basis for perturbation methods
Statistics and Computing
Remote access systems for statistical analysis of microdata
Statistics and Computing
Spatial and non-spatial model-based protection procedures for the release of business microdata
Statistics and Computing
Maximum entropy simulation for microdata protection
Statistics and Computing
Remote access systems for statistical analysis of microdata
Statistics and Computing
Secure computation with horizontally partitioned data using adaptive regression splines
Computational Statistics & Data Analysis
Computer Methods and Programs in Biomedicine
Verification servers: Enabling analysts to assess the quality of inferences from public use data
Computational Statistics & Data Analysis
Regression output from a remote analysis server
Data & Knowledge Engineering
The microdata analysis system at the U.S. census bureau
PSD'10 Proceedings of the 2010 international conference on Privacy in statistical databases
“Secure” log-linear and logistic regression analysis of distributed databases
PSD'06 Proceedings of the 2006 CENEX-SDC project international conference on Privacy in Statistical Databases
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To protect public-use microdata, one approach is not to allow users access to the microdata. Instead, users submit analyses to a remote computer that reports back basic output from the fitted model, such as coefficients and standard errors. To be most useful, this remote server also should provide some way for users to check the fit of their models, without disclosing actual data values. This paper discusses regression diagnostics for remote servers. The proposal is to release synthetic diagnostics—i.e. simulated values of residuals and dependent and independent variables–constructed to mimic the relationships among the real-data residuals and independent variables. Using simulations, it is shown that the proposed synthetic diagnostics can reveal model inadequacies without substantial increase in the risk of disclosures. This approach also can be used to develop remote server diagnostics for generalized linear models.