Patent ID: 11922289
Assignee: SUBSALT INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 17:
18. The method according to claim 17, wherein determining the synthetic machine learning regression model of the plurality of synthetic machine learning regression models that performs closest to the distinct machine learning regression model trained on the sensitive data samples includes:
computing a plurality of synthetic mean squared error (MSE) model efficacy metrics for the plurality of synthetic machine learning regression models trained on the synthetic data samples based on predictive inferences of the plurality of synthetic machine learning regression models on a validation dataset derived from the target sensitive dataset,
computing a raw data mean squared error (MSE) model efficacy metric for the distinct machine learning regression model trained on the sensitive data samples based on predictive inferences of the distinct machine learning regression model on the validation dataset derived from the target sensitive dataset,
computing a plurality of derivative synthetization efficacy metrics based on a difference between the raw data MSE model efficacy metric and a distinct one of the plurality of synthetic MSE model efficacy metrics, and
selecting a synthetic machine learning regression model associated with a smallest derivative synthetization efficacy metric as the synthetic machine learning regression model that performs closest to the distinct machine learning regression model trained on the sensitive data samples.