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Decision trees and random forests are common classifiers with widespread use. In this paper, we develop two protocols for privately evaluating decision trees and random forests. We operate in the standard two-party setting where the server holds a model (either a tree or a forest), and the client holds an input (a feature vector). At the conclusion of the protocol, the client learns only the model's output on its input and a few generic parameters concerning the model; the server learnsdoi:10.1515/popets-2016-0043 dblp:journals/popets/WuFNL16 fatcat:3rjbn2om3zh7ljvgaugfcatt24