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Non-Interactive Private Decision Tree Evaluation

In this paper, we address the problem of privately evaluating a decision tree on private data. This scenario consists of a server holding a private decision tree model and a client interested in classifying its private attribute vector using the server’s private model. The goal is to obtain the classification while preserving the privacy of both.  Download the Document

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