Privacy-preserving representation machine learning by disentanglement

A method comprising:

• obtaining labelled training data having labels showing values of private attributes identifiable from the data;
• passing the labelled training data into a variational autoencoder (VAE) having loss terms for a loss of predictability of private attributes from public information in the labelled training data and a loss of predictability of public attributes from the public information in the labelled training data, outputting a training representation;
• feeding the training representation into a feed forward neural network to train the feed forward neural network to compress data while minimizing the loss of predictability of public attributes from the public information while also maximizing the loss of predictability of private attributes from the public information;
• and
• maintaining the variational autoencoder and the feed forward neural network on a trusted server separate and distinct from a third-party server having a machine learning algorithm trained using data output by the feed forward neural network.

A non-transitory machine-readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform operations comprising:

• obtaining labelled training data having labels showing values of private attributes identifiable from the data;
• passing the labelled training data into a variational autoencoder (VAE) having loss terms for a loss of predictability of private attributes from public information in the labelled training data and a loss of predictability of public attributes from the public information in the labelled training data, outputting a training representation;
• feeding the training representation into a feed forward neural network to train the feed forward neural network to compress data while minimizing the loss of predictability of public attributes from the public information while also maximizing the loss of predictability of private attributes from the public information;
• and
• maintaining the variational autoencoder and the feed forward neural network on a trusted server separate and distinct from a third-party server having a machine learning algorithm trained using data output by the feed forward neural network.