Privacy-preserving decision trees evaluation via linear functions

Abstract

The combination of cloud-based computing paradigm and machine learning algorithms has enabled many complex analytic services, such as face recognition in a crowd or valuation of immovable properties. Companies can charge clients who do not have the expertise or resource to build such complex models for the prediction or classification service. In this work, we focus on machine learning classification with decision tree (or random forests) as the analytic model, which is popular for its effectiveness and simplicity. We propose privacy-preserving decision tree evaluation protocols which hide the sensitive inputs (model and query) from the counterparty. Comparing with the state-of-the-art, we made a significant improvement in efficiency by cleverly exploiting the structure of decision trees, which avoids an exponential number of encryptions in the depth of the decision tree. Our experiment results show that our protocols are especially efficient for deep but sparse decision trees, which are typical for classification models trained from real datasets, ranging from cancer diagnosis to spam classification.