Arizona State University

Ghazaleh Beigi

Huan Liu

Ruocheng Guo

Kai Shu

Yanchao Zhang

Suhang Wang

Alexander Nou

Ahmadreza Mosallanezhad

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.

Systems and methods for utility-preserving deep reinforcement learning-based text anonymization

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.

Systems and methods for a privacy preserving text representation learning framework

Systems and methods of privacy protection of a user may include compiling an actual number of links in a web history corresponding to each topic in a plurality of internet topics; compiling a topic probability distribution based on the web history; determining an additional number of links to be added to each topic in the plurality of internet topics; and modifying the topic probability distribution by selecting a set of links corresponding to the additional number of links for each topic in the plurality of internet topics.

Privacy protection systems and methods

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Tempe, AZ, United States of America

Ghazaleh Beigi