A computer implemented method for protecting a dataset, the method comprising retrieving a first dataset; identifying first distributional characteristics of the first dataset; generating, based on the first distributional characteristics, a second dataset, wherein the number of samples in the first dataset is smaller than the number of samples in the second dataset; generating a combined dataset based on the first dataset and on the second dataset, wherein generating the combined dataset further comprises: interleaving a first plurality of samples in the first dataset among a second plurality of samples in the second dataset; assigning a first source identifier to each of the first plurality of samples; and assigning a second source identifier to each of the second plurality of samples, wherein identifying the portion of the output corresponding to the first dataset comprises identifying the portion of the output corresponding to the first dataset based on the first source identifier and on the second source identifier; transmitting, over a network, the combined dataset as an input to a trained machine learning model; receiving over the network output from the trained machine learning model that was generated based on the combined dataset input; identifying a portion of the output corresponding to the first dataset; and determining a performance metric of the trained machine learning model based on the portion of the output corresponding to the first dataset. retrieving a first dataset; identifying first distributional characteristics of the first dataset; generating, based on the first distributional characteristics, a second dataset, wherein the number of samples in the first dataset is smaller than the number of samples in the second dataset; generating a combined dataset based on the first dataset and on the second dataset, wherein generating the combined dataset further comprises: interleaving a first plurality of samples in the first dataset among a second plurality of samples in the second dataset; assigning a first source identifier to each of the first plurality of samples; and assigning a second source identifier to each of the second plurality of samples, interleaving a first plurality of samples in the first dataset among a second plurality of samples in the second dataset; assigning a first source identifier to each of the first plurality of samples; and assigning a second source identifier to each of the second plurality of samples, wherein identifying the portion of the output corresponding to the first dataset comprises identifying the portion of the output corresponding to the first dataset based on the first source identifier and on the second source identifier; transmitting, over a network, the combined dataset as an input to a trained machine learning model; receiving over the network output from the trained machine learning model that was generated based on the combined dataset input; identifying a portion of the output corresponding to the first dataset; and determining a performance metric of the trained machine learning model based on the portion of the output corresponding to the first dataset. transmitting, over a network, the combined dataset as an input to a trained machine learning model; receiving over the network output from the trained machine learning model that was generated based on the combined dataset input; identifying a portion of the output corresponding to the first dataset; and determining a performance metric of the trained machine learning model based on the portion of the output corresponding to the first dataset. <EOS>