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Private yet Efficient Decision Tree Evaluation

Joye, Marc and Salehi, Fariborz (2018) Private yet Efficient Decision Tree Evaluation. In: Data and Applications Security and Privacy XXXII. Lecture Notes in Computer Science. No.10980. Springer , Cham, pp. 243-259. ISBN 978-3-319-95728-9. https://resolver.caltech.edu/CaltechAUTHORS:20191025-160611595

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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.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1007/978-3-319-95729-6_16DOIArticle
Additional Information:© 2018 IFIP International Federation for Information Processing. First Online: 10 July 2018.
Subject Keywords:Data mining; Privacy; Integer comparison; Decision trees
Series Name:Lecture Notes in Computer Science
Issue or Number:10980
Record Number:CaltechAUTHORS:20191025-160611595
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20191025-160611595
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:99472
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:25 Oct 2019 23:18
Last Modified:25 Oct 2019 23:18

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