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US 11,907,666 B2Granted
Systems and methods for utility-preserving deep reinforcement learning-based text anonymization
AssigneeArizona Board of Regents on behalf of Arizona State University
InventorsAhmadreza Mosallanezhad, Ghazaleh Beigi, Huan Liu
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Abstract
Various embodiments of a system and associated method for anonymization of text without losing semantic utility of text by extracting a latent embedding representation of content with respect to a given task and by learning an optimal strategy for text embedding manipulation to satisfy both privacy and utility requirements are disclosed herein. In particular, the system balances private attribute obfuscation with retained semantic utility.
Claims20
A system, comprising:
- a processor in communication with a memory, the memory including instructions which, when executed, cause the processor to:
- iteratively apply, by a computer-implemented learning agent, an action to a randomly selected text embedding representation of a textual document based on a current state of the computer-implemented learning agent and an expected return;
- and
- iteratively update a reward value for a new state, wherein the reward value is determined based on a private attribute confidence of a private attribute classifier component and a utility confidence of a utility classifier component;
- wherein the computer-implemented learning agent seeks to apply the action to the randomly selected text embedding such that the reward value is maximized as the private attribute confidence is minimized and the utility confidence is maximized.
2 more elements
A method, comprising:
- iteratively applying, by a computer-implemented learning agent, an action to a randomly selected text embedding representation of a textual document based on a current state of the computer-implemented learning agent and an expected reward;
- and
- iteratively updating a reward value for a new state, wherein the reward value is determined based on a private attribute confidence of a private attribute classifier component and a utility confidence of a utility classifier component;
- wherein the computer-implemented learning agent seeks to apply the action to the randomly selected text embedding such that the reward value is maximized as the private attribute confidence is minimized and the utility confidence is maximized.
1 more element
Dependencies are inferred from the claim text.