  This paper introduces a privacy-aware Bayesian approach that combines
ensembles of classifiers and clusterers to perform semi-supervised and
transductive learning. We consider scenarios where instances and their
classification/clustering results are distributed across different data sites
and have sharing restrictions. As a special case, the privacy aware computation
of the model when instances of the target data are distributed across different
data sites, is also discussed. Experimental results show that the proposed
approach can provide good classification accuracies while adhering to the
data/model sharing constraints.
