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公开(公告)号：US20240046399A1
公开(公告)日：2024-02-08
申请号：US18489399
申请日：2023-10-18
Applicant: Adobe Inc.
Inventor： Irgelkha Mejia , Ronald Oribio , Robert Burke , Michele Saad
CPC classification number: G06Q50/265 , G06F16/48 , G06F40/40 , G06F21/6245 , G06N3/08 , G06Q10/0635 , G06Q10/10 , G06Q50/01 , G06F3/0482
Abstract: Systems and methods use machine learning models with content editing tools to prevent or mitigate inadvertent disclosure and dissemination of sensitive data. Entities associated with private information are identified by applying a trained machine learning model to a set of unstructured text data received via an input field of an interface. A privacy score is computed for the text data by identifying connections between the entities, the connections between the entities contributing to the privacy score according to a cumulative privacy risk, the privacy score indicating potential exposure of the private information. The interface is updated to include an indicator distinguishing a target portion of the set of unstructured text data within the input field from other portions of the set of unstructured text data within the input field, wherein a modification to the target portion changes the potential exposure of the private information indicated by the privacy score.
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公开(公告)号：US11830099B2
公开(公告)日：2023-11-28
申请号：US17093175
申请日：2020-11-09
Applicant: Adobe Inc.
Inventor： Irgelkha Mejia , Ronald Oribio , Robert Burke , Michele Saad
IPC: G06Q10/10 , G06Q10/06 , G06Q30/06 , G06Q30/02 , G06Q40/08 , G06Q50/26 , G06F16/48 , G06F40/40 , G06F21/62 , G06N3/08 , G06Q10/0635 , G06Q50/00 , G06F3/0482
CPC classification number: G06Q50/265 , G06F16/48 , G06F21/6245 , G06F40/40 , G06N3/08 , G06Q10/0635 , G06Q10/10 , G06Q50/01 , G06F3/0482
Abstract: Systems and methods use machine learning models with content editing tools to prevent or mitigate inadvertent disclosure and dissemination of sensitive data. Entities associated with private information are identified by applying a trained machine learning model to a set of unstructured text data received via an input field of an interface. A privacy score is computed for the text data by identifying connections between the entities, the connections between the entities contributing to the privacy score according to a cumulative privacy risk, the privacy score indicating potential exposure of the private information. The interface is updated to include an indicator distinguishing a target portion of the set of unstructured text data within the input field from other portions of the set of unstructured text data within the input field, wherein a modification to the target portion changes the potential exposure of the private information indicated by the privacy score.
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公开(公告)号：US20220148113A1
公开(公告)日：2022-05-12
申请号：US17093175
申请日：2020-11-09
Applicant: Adobe Inc.
Inventor： Irgelkha Mejia , Ronald Oribio , Robert Burke , Michele Saad
Abstract: Systems and methods use machine learning models with content editing tools to prevent or mitigate inadvertent disclosure and dissemination of sensitive data. Entities associated with private information are identified by applying a trained machine learning model to a set of unstructured text data received via an input field of an interface. A privacy score is computed for the text data by identifying connections between the entities, the connections between the entities contributing to the privacy score according to a cumulative privacy risk, the privacy score indicating potential exposure of the private information. The interface is updated to include an indicator distinguishing a target portion of the set of unstructured text data within the input field from other portions of the set of unstructured text data within the input field, wherein a modification to the target portion changes the potential exposure of the private information indicated by the privacy score.
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