Non-Interactive Private Decision Tree Evaluation

@inproceedings{Tueno2020NonInteractivePD,
  title={Non-Interactive Private Decision Tree Evaluation},
  author={Anselme Tueno and Yordan Boev and Florian Kerschbaum},
  booktitle={DBSec},
  year={2020}
}
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 the… 
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