Privacy-Preserving Tree-Based Inference with TFHE

Fully Homomorphic Encryption is a powerful tool for processing encrypted data and is particularly adapted to the type of programs that are common in machine learning (ML). On tabular data, tree-based ML models obtain state-of-the-art results, are more robust, and are easier to use and deploy than neural networks. We introduce an implementation of privacy-preserving decision tree evaluation based on the TFHE scheme, leveraging optimized representations for encrypted integer and TFHE’s powerful programmable bootstrapping mechanism. Our technique is applicable to decision trees, random forests, and gradient boosted trees. We demonstrate our approach on popular datasets and show that accuracy on encrypted data is very close the one obtained by the same models applied to clear data, while latency is competitive with the state of the art.