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Published July 10, 2018 | public
Book Section - Chapter

Private yet Efficient Decision Tree Evaluation

Abstract

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 comparisons 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.

Additional Information

© 2018 IFIP International Federation for Information Processing. First Online: 10 July 2018.

Additional details

Created:
August 21, 2023
Modified:
January 14, 2024