A Privacy-Aware Bayesian Approach for Combining Classifier and Cluster Ensembles
This addresses privacy concerns for distributed data scenarios, but it appears incremental as it builds on existing ensemble methods with privacy adaptations.
The paper tackles the problem of performing semi-supervised and transductive learning with distributed data under privacy constraints, and the result is that the proposed approach achieves good classification accuracies while adhering to sharing restrictions.
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.