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Applying machine learning algorithms to private data, such as financial ormedical data, while preserving their confidentiality, is a difficult task.Homomorphic Encryption (HE) is acknowledged for its ability to allowcomputation on encrypted data, where both the input and output are encrypted,which therefore enables secure inference on private data. Nonetheless, becauseof the constraints of HE, such as its inability to evaluate non-polynomialfunctions or to perform arbitrary matrix multiplication efficiently, onlyinference of linear models seem usable in practice in the HE paradigm so far.  In this paper, we propose Cryptotree, a framework that enables the use ofRandom Forests (RF), a very powerful learning procedure compared to linearregression, in the context of HE. To this aim, we first convert a regular RF toa Neural RF, then adapt this to fit the HE scheme CKKS, which allows HEoperations on real values. Through SIMD operations, we are able to have quickinference and prediction results better than the original RF on encrypted data.

Cryptotree: fast and accurate predictions on encrypted structured data