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Lecture Notes in Computer Science
Decision trees are a popular method for a variety of machine learning tasks. A typical application scenario involves a client providing a vector of features and a service provider (server) running a trained decision-tree model on the client's vector. Both inputs need to be kept private. In this work, we present efficient protocols for privately evaluating decision trees. Our design reduces the complexity of existing solutions with a more interactive setting, which improves the total number ofdoi:10.1007/978-3-319-95729-6_16 fatcat:mgz4k5optfdbnesesf55kjhrwy