Systems and methods for providing privacy protection and utility preservation in multi-attribute data transformation with theoretical proofs

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 a raw dataset as input to a neural network θ, and wherein the neural network θ outputs a transformed dataset;
• provide a sensitive attribute suppression module of the machine learning model, wherein the sensitive attribute suppression module accepts the raw dataset as input to a neural network ϕ, accepts the transformed dataset as input to a neural network ϕ′, and calculates, for each attribute of a plurality of annotated sensitive attributes S, 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 raw dataset as input to a neural network ψ, accepts the transformed dataset as input to a neural network ψ′, and calculates, for each attribute of a plurality of annotated useful attributes U, an annotated useful attribute preservation loss;
• provide an unannotated useful attribute preservation module of the machine learning model, wherein the unannotated useful attribute preservation module accepts the transformed dataset and the raw dataset as input to a neural network η, and calculates, for an unannotated useful attribute F, an unannotated useful attribute preservation loss;
• combine the sensitive attribute suppression loss, the annotated useful attribute preservation loss, and the unannotated useful attribute preservation loss into a total loss;
• and
• train the neural network θ and the neural network η using the total loss.

A non-transitory computer readable storage medium, including instructions stored thereon, which instructions, 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 on at least one computer comprising a processor and a memory;
• providing a data transformation module of the machine learning model, wherein the data transformation module accepts a raw dataset as input to a neural network θ, and wherein the neural network θ outputs a transformed dataset;
• providing a sensitive attribute suppression module of the machine learning model, wherein the sensitive attribute suppression module accepts the raw dataset as input to a neural network ϕ, accepts the transformed dataset as input to a neural network ϕ′, and calculates, for each attribute of a plurality of annotated sensitive attributes S, 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 raw dataset as input to a neural network ψ, accepts the transformed dataset as input to a neural network ψ′, and calculates, for each attribute of a plurality of annotated useful attributes U, an annotated useful attribute preservation loss;
• providing an unannotated useful attribute preservation module of the machine learning model, wherein the unannotated useful attribute preservation module accepts the transformed dataset and the raw dataset as input to a neural network η, and calculates, for an unannotated useful attribute F, an unannotated useful attribute preservation loss;
• combining the sensitive attribute suppression loss, the annotated useful attribute preservation loss, and the unannotated useful attribute preservation loss into a total loss;
• and
• training the neural network θ and the neural network η using the total loss.