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Lecture Notes in Computer Science
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 of the computation is to obtain the classification while preserving the privacy of both-the decision tree and the client input. After the computation, the client learns the classification result and nothing else, and thedoi:10.1007/978-3-030-49669-2_10 fatcat:wzomzihvwjdqxlznwezejwar6u