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Secure Bayesian model averaging for horizontally partitioned data. (English) Zbl 1322.62101
Summary: When multiple data owners possess records on different subjects with the same set of attributes – known as horizontally partitioned data – the data owners can improve analyses by concatenating their databases. However, concatenation of data may be infeasible because of confidentiality concerns. In such settings, the data owners can use secure computation techniques to obtain the results of certain analyses on the integrated database without sharing individual records. We present secure computation protocols for Bayesian model averaging and model selection for both linear regression and probit regression. Using simulations based on genuine data, we illustrate the approach for probit regression, and show that it can provide reasonable model selection outputs.

MSC:
62F15 Bayesian inference
62J02 General nonlinear regression
62J12 Generalized linear models (logistic models)
62-07 Data analysis (statistics) (MSC2010)
Software:
BAS; tsbridge
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