Privacy-preserving approach to bayesian network structure learning from distributed data
Olivier Regnier-Coudert, John McCall · 2011
In many situations, data is scattered across different sites, making the modeling process difficult or sometimes impossible. Some applications could benefit from collaborations between organisations but data security or privacy policies often act as a barrier to data mining on such contexts. In this paper, we present a novel approach to learning Bayesian Networks (BN) structures from multiple datasets, based on the use of Ensembles and an Island Model Genetic Algorithm (IMGA). The proposed design ensures no data is shared during the process and can fit many applications.