Private yet Efficient Decision Tree Evaluation [chapter]

Marc Joye, Fariborz Salehi
2018 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 of
more » ... mparisons to evaluate the decision tree. It crucially uses oblivious transfer protocols and leverages their amortized overhead. Furthermore, and of independent interest, we improve by roughly a factor of two the DGK comparison protocol.
doi:10.1007/978-3-319-95729-6_16 fatcat:mgz4k5optfdbnesesf55kjhrwy