Privacy driven data subset sizing

Abstract

Techniques are described for maintaining patient privacy in association with obtaining patient data for machine learning applications. In an example, a method can comprise accessing, by a system comprising a processor, a training dataset associated with a machine learning model, the training dataset comprising of data samples respectively comprising unique characteristics of subjects. The method further comprises determining, by the system, characteristic curve information that correlates different data portions extracted from the data samples to respective probabilities of matching the different data portions to respective ones of the data samples from which they are extracted. The method further comprises controlling, by the system, collection of new data portions extracted from new data samples corresponding to the data samples based on the new data portions conforming to criteria that defines a target data portion, wherein the criteria comprise a probability of the respective probabilities that satisfies an anonymity criterion.