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US 11,763,093 B2Granted
Systems and methods for a privacy preserving text representation learning framework
AssigneeArizona Board of Regents on behalf of Arizona State University
InventorsGhazaleh Beigi, Kai Shu, Ruocheng Guo, Suhang Wang, Huan Liu
Life of this patent
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Abstract
Various embodiments of a computer-implemented system which learns textual representations while filtering out potentially personally identifying data and retaining semantic meaning within the textual representations are disclosed herein.
Claims18
A method of generating a modified latent text representation for a document, comprising:
- utilizing a processor in communication with a tangible storage medium storing instructions that are executed by the processor to perform operations comprising:
- generating an initial latent representation representative of text in a document;
- inferring an amount of noise to be added to the initial latent representation by finding a privacy budget value that minimizes a loss between a predicted semantic label and a ground truth semantic label for the initial latent representation and maximizes a loss between a predicted private attribute label and a ground truth private attribute label for the initial latent representation;
- adding the amount of noise to the initial latent text representation to generate the modified latent text representation;
- optimize a set of private attribute discriminator parameters and the privacy budget value that maximize the loss between the predicted private attribute label and the ground truth private attribute label for the initial latent representation;
- generating the predicted private attribute label by applying a classifier to the modified latent representation;
- maximizing a loss between the predicted private attribute label and the ground truth private attribute label;
- and
- selecting the set of private attribute discriminator classifier parameters associated with the lowest loss value between the predicted private attribute label and the ground truth private attribute label.
6 more elements
A computer system for generating a modified latent text representation for a document, comprising:
- at least one processor in communication with a memory and operable for execution of a plurality of modules, the plurality of modules including:
- an auto-encoder configured to generate an initial latent representation representative of text in a document;
- a noise adder module configured to receive the initial latent representation and add an amount of noise to the initial latent representation to generate a modified latent text representation based on a privacy budget value;
- a semantic meaning discriminator module configured to optimize a set of semantic discriminator classifier parameters and the privacy budget value such that a loss is minimized between the predicted semantic label and the ground truth semantic label for the initial latent representation;
- and
- a private attribute discriminator module configured to optimize a set of private attribute discriminator parameters and the privacy budget value such that a loss is maximized between the predicted private attribute label and the ground truth private attribute label for the initial latent representation.
3 more elements
Dependencies are inferred from the claim text.