We present an efficient protocol for the privacy-preserving, distributed learning of decision-tree classifiers. Our protocol allows a user to construct a classifier on a database h...
Many approaches to active learning involve training one classifier by periodically choosing new data points about which the classifier has the least confidence, but designing a co...