Privacy-preserving approach to bayesian network structure learning from distributed data

Authors:
Olivier Regnier-Coudert;John McCall
Affiliations:
Robert Gordon University, Aberdeen, United Kingdom;Robert Gordon University, Aberdeen, United Kingdom
Venue:
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Year:
2011

Citing 4
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A chain-model genetic algorithm for Bayesian network structure learning

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Abstract

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.