Minimal trust data sharing

Abstract

A computer-implemented method of protecting confidentiality when generating synthetic training records for machine learning from sensitive data records, comprising a source computer system connected to a separate target computer system, where the target computer system comprises sensitive data records comprising private or confidential data. The source computer system performs the functions of a generator component of a generative adversarial network (GAN) and the target computer system performs the functions of a discriminator component of the GAN, where the generator and discriminator functions of the GAN are distributed between the source and target computer systems. Synthetic training records are generated using a computational process that does not reveal contents of the sensitive data records. Also disclosed is a method of training a machine-learning model using the one or more synthetic training records.

CPC Classifications