Journal ArticleDOI
Model Diagnostics for Remote Access Regression Servers
TLDR
The proposed synthetic diagnostics can reveal model inadequacies without substantial increase in the risk of disclosures, and can be used to develop remote server diagnostics for generalized linear models.Abstract:
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.read more
Citations
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Journal ArticleDOI
Secure Regression on Distributed Databases
TL;DR: This article presents several methods for performing linear regression on the union of distributed databases that preserve, to varying degrees, confidentiality of those databases.
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Secure multiple linear regression based on homomorphic encryption
TL;DR: This work conceptualizes the existence of a single combined database containing all of the information for the individuals in the separate databases and for the union of the variables, and proposes an approach that gives full statistical calculation on this combined database without actually combining information sources.
Journal ArticleDOI
Data Dissemination and Disclosure Limitation in a World Without Microdata: A Risk-Utility Framework for Remote Access Analysis Servers
TL;DR: In this paper, the authors describe the risks and propose quantifiable measures of risk and data utility that can be used to specify which queries can be answered, and with what output, for regression models.
Journal ArticleDOI
Secure, Privacy-Preserving Analysis of Distributed Databases.
TL;DR: This article shows how tools from information technology—specifically, secure multiparty computation and networking—can be used to perform statistically valid analyses of distributed databases, and presents protocols for securely performing regression, maximum likelihood estimation, and Bayesian analysis.
Journal ArticleDOI
Privacy and the use of health data for research.
TL;DR: Resources for researchers interested in privacy issues surrounding secondary use of health data for research, including applicable privacy regulations and available information on privacy perception in Australia are reviewed.
References
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Journal ArticleDOI
Robust Locally Weighted Regression and Smoothing Scatterplots
TL;DR: Robust locally weighted regression as discussed by the authors is a method for smoothing a scatterplot, in which the fitted value at z k is the value of a polynomial fit to the data using weighted least squares, where the weight for (x i, y i ) is large if x i is close to x k and small if it is not.
OtherDOI
Generalized Additive Models
Trevor Hastie,Robert Tibshirani +1 more
TL;DR: The generalized additive model (GA) as discussed by the authors is a generalization of the generalized linear model, which replaces the linear model with a sum of smooth functions in an iterative procedure called local scoring algorithm.
Book
Elements of Statistical Disclosure Control
Leon Willenborg,Ton de Waal +1 more
TL;DR: This chapter discusses the development of SDC techniques for Microdata and Tables in the rapidly changing environment, as well as some of the techniques used in this area's initial development.