Method and system for adaptively reducing feature bit-size for homomorphically encrypted data sets used to train machine learning models

Abstract

Certain aspects of the present disclosure provide techniques for adaptively reducing the bit size of features in a training data set used to train a machine learning model. An example method generally includes receiving a data set to be used in training a machine learning model and a definition of the machine learning model to be trained. A reduced number of bits to represent features in the data set is determined based on values of each feature in the data set and the definition of the machine learning model. A reduced bit-size data set is generated by reducing a bit size of each feature in the data set according to the reduced number of bits, and the reduced bit-size data set is encrypted using a homomorphic encryption scheme. A machine learning model is trained based on the encrypted reduced bit-size data set.