公开(公告)号：US11816243B2

公开(公告)日：2023-11-14

申请号：US17397407

申请日：2021-08-09

Applicant: Adobe Inc.

Inventor： Thi Kim Phung Lai ,  Tong Sun ,  Rajiv Jain ,  Nikolaos Barmpalios ,  Jiuxiang Gu ,  Franck Dernoncourt

IPC: G06F21/62 ,  G06N20/00 ,  G06F40/295

CPC classification number: G06F21/6245 ,  G06F40/295 ,  G06N20/00

Abstract: Systems, methods, and non-transitory computer-readable media can generate a natural language model that provides user-entity differential privacy. For example, in one or more embodiments, a system samples sensitive data points from a natural language dataset. Using the sampled sensitive data points, the system determines gradient values corresponding to the natural language model. Further, the system generates noise for the natural language model. The system generates parameters for the natural language model using the gradient values and the noise, facilitating simultaneous protection of the users and sensitive entities associated with the natural language dataset. In some implementations, the system generates the natural language model through an iterative process (e.g., by iteratively modifying the parameters).

公开(公告)号：US20230059367A1

公开(公告)日：2023-02-23

申请号：US17397407

申请日：2021-08-09

Applicant: Adobe Inc.

Inventor： Thi Kim Phung Lai ,  Tong Sun ,  Rajiv Jain ,  Nikolaos Barmpalios ,  Jiuxiang Gu ,  Franck Dernoncourt

IPC: G06F21/62 ,  G06F40/295 ,  G06N20/00

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate a natural language model that provides user-entity differential privacy. For example, in one or more embodiments, the disclosed systems sample sensitive data points from a natural language dataset. Using the sampled sensitive data points, the disclosed systems determine gradient values corresponding to the natural language model. Further, the disclosed systems generate noise for the natural language model. The disclosed systems generate parameters for the natural language model using the gradient values and the noise, facilitating simultaneous protection of the users and sensitive entities associated with the natural language dataset. In some implementations, the disclosed systems generate the natural language model through an iterative process (e.g., by iteratively modifying the parameters).