Secret sharing homomorphisms: keeping shares of a secret secret
Proceedings on Advances in cryptology---CRYPTO '86
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
On Bayesian model and variable selection using MCMC
Statistics and Computing
Privacy Preserving Data Mining
CRYPTO '00 Proceedings of the 20th Annual International Cryptology Conference on Advances in Cryptology
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-preserving k-means clustering over vertically partitioned data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy preserving mining of association rules
Information Systems - Knowledge discovery and data mining (KDD 2002)
Privacy-preserving clustering with distributed EM mixture modeling
Knowledge and Information Systems
Secure computation with horizontally partitioned data using adaptive regression splines
Computational Statistics & Data Analysis
Secure Logistic Regression of Horizontally and Vertically Partitioned Distributed Databases
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Knowledge discovery approach to automated cardiac SPECT diagnosis
Artificial Intelligence in Medicine
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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.