  Learning a classifier from private data collected by multiple parties is an
important problem that has many potential applications. How can we build an
accurate and differentially private global classifier by combining
locally-trained classifiers from different parties, without access to any
party's private data? We propose to transfer the `knowledge' of the local
classifier ensemble by first creating labeled data from auxiliary unlabeled
data, and then train a global $\epsilon$-differentially private classifier. We
show that majority voting is too sensitive and therefore propose a new risk
weighted by class probabilities estimated from the ensemble. Relative to a
non-private solution, our private solution has a generalization error bounded
by $O(\epsilon^{-2}M^{-2})$ where $M$ is the number of parties. This allows
strong privacy without performance loss when $M$ is large, such as in
crowdsensing applications. We demonstrate the performance of our method with
realistic tasks of activity recognition, network intrusion detection, and
malicious URL detection.
