Privacy preserving synthetic string generation using recurrent neural networks

A system comprising:

• a processor;
• a memory coupled to the processor;
• a physical storage device;
• the memory comprising an application, wherein the application executes on the processor, uses the memory, and is configured for:
• obtaining, from the physical storage device, training data comprising a plurality of training strings comprising private information;
• training a recurrent neural network (RNN) model with the plurality of training strings to predict a next character in a training string, to obtain a trained RNN model, wherein training the RNN model comprises:
• applying an embedded layer to the training data to generate an embedded layer output, wherein the embedded layer receives as input an encoded vector that represents a character from a string from the training data, wherein the embedded layer output is an embedded vector that includes information about the character within a threshold number of characters of the character represented by the encoded vector,
• applying a plurality of RNN layers to the embedded layer output to generate a recurrent layer output, wherein the recurrent layer output predicts a next character from a sequence of input characters encoded with the embedded layer,
• applying a fully connected layer to the recurrent layer output to generate a fully connected layer output, wherein the fully connected layer output predicts the next character in the string from the training data that is inputted to the RNN, and
• comparing the fully connected layer output to an expected output using a loss function that identifies a difference between an output of the RNN model and the expected output, wherein the difference is then fed back into the plurality of RNN layers of the RNN model to update weights of the RNN model;
• receiving a request to generate an anonymous string from a plurality of input strings comprising private information;
• selecting a prime character randomly based on a frequency of the prime character as a starting character in the plurality of input strings;
• processing, by the trained RNN model, the selected prime character to generate the anonymous string, wherein the prime character is used as the starting character for each of the plurality of input strings, wherein the prime character is a seed character that starts a generation of an output string with the RNN model;
• generating a list of invalid terms from the private information corresponding to the plurality of input strings;
• comparing at least one string term from the anonymous string to the list of invalid terms;
• validating the anonymous string responsive to the anonymous string precluding the list of invalid terms to obtain a validated anonymous string;
• and
• transmitting the validated anonymous string in response to the request for the anonymous string.

A set of one or more non-transitory computer readable mediums comprising computer readable program code for:

• obtaining, from a physical storage device, training data comprising a plurality of training strings comprising private information;
• training a recurrent neural network (RNN) model with the plurality of training strings to predict a next character in a training string, to obtain a trained RNN model, wherein training the RNN model comprises:
• applying an embedded layer to the training data to generate an embedded layer output, wherein the embedded layer receives as input an encoded vector that represents a character from a string from the training data, wherein the embedded layer output is an embedded vector that includes information about the character within a threshold number of characters of the character represented by the encoded vector,
• applying a plurality of RNN layers to the embedded layer output to generate a recurrent layer output, wherein the recurrent layer output predicts a next character from a sequence of input characters encoded with the embedded layer,
• applying a fully connected layer to the recurrent layer output to generate a fully connected layer output, wherein the fully connected layer output predicts the next character in the string from the training data that is inputted to the RNN, and
• comparing the fully connected layer output to an expected output using a loss function that identifies a difference between the an output of the RNN model and the expected output, wherein the difference is then fed back into the plurality of RNN layers of the RNN model to update weights of the RNN model;
• receiving a request to generate an anonymous string from a plurality of input strings comprising private information;
• selecting a prime character randomly based on a frequency of the prime character as a starting character in the plurality of input strings;
• processing, by the trained RNN model, the selected prime character to generate the anonymous string, wherein the prime character is used as the starting character for each of the plurality of input strings, wherein the prime character is a seed character that starts a generation of an output string with the RNN model;
• generating a list of invalid terms from the private information corresponding to the plurality of input strings;
• comparing at least one string term from the anonymous string to the list of invalid terms;
• validating the anonymous string responsive to the anonymous string precluding the list of invalid terms to obtain a validated anonymous string;
• and
• transmitting the validated anonymous string in response to the request for the anonymous string.