MACHINE LEARNING TECHNIQUES FOR MITIGATING AGGREGATE EXPOSURE OF IDENTIFYING INFORMATION

    公开(公告)号:US20220286438A1

    公开(公告)日:2022-09-08

    申请号:US17195349

    申请日:2021-03-08

    Applicant: Adobe Inc.

    Abstract: Systems and methods mitigate aggregate exposure of identifying information using machine learning. A privacy monitoring system identifies entities and corresponding entity types by applying a set of domain-specific neural networks, each trained to recognize a particular entity type, to media data extracted from two or more content items associated with a user. The privacy monitoring system computes a privacy score indicating a cumulative privacy risk for potential exposure of identifying information associated with the user from the two or more content items by identifying connections between the identified entities. The connections between the entities are weighted according to the entity types and contribute to the privacy score. A reporting subsystem outputs an indication of a recommended action for mitigating the cumulative privacy risk.

    MACHINE LEARNING COLLABORATION TECHNIQUES

    公开(公告)号:US20240420212A1

    公开(公告)日:2024-12-19

    申请号:US18335921

    申请日:2023-06-15

    Applicant: Adobe Inc.

    Abstract: A feedback management subsystem receives, from a first user, first text comprising commentary on an item. The feedback management subsystem receives, from the first user, instructions to request commentary on the item from a second user. Responsive to receiving the instructions to request commentary from the second user, a communication subsystem transmits a notification to the second user. The feedback management subsystem receives, from the second user, second text comprising commentary on the item. A first machine learning model performs sentiment analysis to identify sentiments of the first text and the second text. A recommendation subsystem identifies prior actions of the first user and associated sentiments of the second user. A second machine learning model identifies a second item based on the prior actions of the first user and the sentiments of the second user. The recommendation subsystem provides output to the first user recommending the second item.

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