The article presents a method to tune hyperparameters in machine learning models while protecting sensitive data. By using only a subset of the data and extrapolating results, the method reduces the amount of private information leaked and the computational burden. The researchers found that their approach consistently achieves a better balance between privacy and model performance compared to existing methods.