Systems and methods for generic feature suppression and minimal utility presentation from multi-attribute data

A system comprising at least one computer including a processor and a memory, wherein the at least one computer is configured to execute a machine learning model, and wherein the machine learning model is configured to:

• provide a data transformation module of the machine learning model, wherein the data transformation module accepts an original dataset as input to a first neural network and a second neural network and outputs a transformed dataset;
• provide a sensitive attribute suppression module of the machine learning model, wherein the sensitive attribute suppression module accepts the transformed dataset as input to a third neural network and calculates, for each attribute of a plurality of annotated sensitive attributes, a sensitive attribute suppression loss;
• provide an annotated useful attribute preservation module of the machine learning model, wherein the annotated useful attribute preservation module accepts the transformed dataset as input to a fourth neural network, and calculates, for each attribute of a plurality of annotated useful attributes, a useful attribute preservation loss;
• provide a generic feature suppression module of the machine learning model that accepts parameters of a distribution of a latent variable from the first neural network and calculates, for an unannotated generic attribute, a generic feature suppression loss;
• combine the sensitive attribute suppression loss, the useful attribute preservation loss, and the generic feature suppression loss into a total loss;
• and
• train the first neural network and the second neural network with the total loss.

A non-transitory computer readable storage medium, including instructions stored thereon, which when read and executed by one or more computer processors, cause the one or more computer processors to perform steps comprising:

• executing a machine learning model;
• providing a data transformation module of the machine learning model, wherein the data transformation module accepts an original dataset as input to a first neural network and a second neural network and outputs a transformed dataset;
• providing a sensitive attribute suppression module of the machine learning model, wherein the sensitive attribute suppression module accepts the transformed dataset as input to a third neural network and calculates, for each attribute of a plurality of annotated sensitive attributes, a sensitive attribute suppression loss;
• providing an annotated useful attribute preservation module of the machine learning model, wherein the annotated useful attribute preservation module accepts the transformed dataset as input to a fourth neural network, and calculates, for each attribute of a plurality of annotated useful attributes, a useful attribute preservation loss, wherein the fourth neural network is fixed after it is initialized;
• providing a generic feature suppression module of the machine learning model that accepts parameters of a distribution of a latent variable from the first neural network and calculates, for an unannotated generic attribute, a generic feature suppression loss;
• combining the sensitive attribute suppression loss, the useful attribute preservation loss, and the generic feature suppression loss into a total loss;
• and
• training the first neural network and the second neural network with the total loss.