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公开(公告)号：US20230066545A1
公开(公告)日：2023-03-02
申请号：US17964563
申请日：2022-10-12
Applicant: Google LLC
Inventor： Hugh Brendan McMahan , Kunal Talwar , Li Zhang , Daniel Ramage
Abstract: Systems and methods for learning differentially private machine-learned models are provided. A computing system can include one or more server computing devices comprising one or more processors and one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors cause the one or more server computing devices to perform operations. The operations can include selecting a subset of client computing devices from a pool of available client computing devices; providing a machine-learned model to the selected client computing devices; receiving, from each selected client computing device, a local update for the machine-learned model; determining a differentially private aggregate of the local updates; and determining an updated machine-learned model based at least in part on the data-weighted average of the local updates.
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公开(公告)号：US20220391947A1
公开(公告)日：2022-12-08
申请号：US17892699
申请日：2022-08-22
Applicant: Google LLC
Inventor： Michael Kleber , Gang Wang , Daniel Ramage , Charles Harrison , Josh Karlin , Moti Yung
IPC: G06Q30/02 , G06F16/951 , H04L9/32 , H04L9/08
Abstract: The present disclosure provides systems and methods for content quasi-personalization or anonymized content retrieval via aggregated browsing history of a large plurality of devices, such as millions or billions of devices. A sparse matrix may be constructed from the aggregated browsing history, and dimensionally reduced, reducing entropy and providing anonymity for individual devices. Relevant content may be selected via quasi-personalized clusters representing similar browsing histories, without exposing individual device details to content providers.
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公开(公告)号：US11475350B2
公开(公告)日：2022-10-18
申请号：US15877196
申请日：2018-01-22
Applicant: Google LLC
Inventor： Hugh Brendan McMahan , Kunal Talwar , Li Zhang , Daniel Ramage
Abstract: Systems and methods for learning differentially private machine-learned models are provided. A computing system can include one or more server computing devices comprising one or more processors and one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors cause the one or more server computing devices to perform operations. The operations can include selecting a subset of client computing devices from a pool of available client computing devices; providing a machine-learned model to the selected client computing devices; receiving, from each selected client computing device, a local update for the machine-learned model; determining a differentially private aggregate of the local updates; and determining an updated machine-learned model based at least in part on the data-weighted average of the local updates.
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Patent Agency Ranking
公开(公告)号：US11120102B2
公开(公告)日：2021-09-14
申请号：US17004324
申请日：2020-08-27
Applicant: Google LLC
Inventor： Hugh Brendan McMahan , Jakub Konecny , Eider Brantly Moore , Daniel Ramage , Blaise H. Aguera-Arcas
IPC: G06F17/17 , G06N20/00 , G06F17/11 , G06F30/00 , G06F111/02
Abstract: Systems and methods of determining a global model are provided. In particular, one or more local updates can be received from a plurality of user devices. Each local update can be determined by the respective user device based at least in part on one or more data examples stored on the user device. The one or more data examples stored on the plurality of user devices are distributed on an uneven basis, such that no user device includes a representative sample of the overall distribution of data examples. The local updates can then be aggregated to determine a global model.
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公开(公告)号：US11087362B2
公开(公告)日：2021-08-10
申请号：US16694691
申请日：2019-11-25
Applicant: Google LLC
Inventor： Keith Bonawitz , Daniel Ramage , David Petrou
Abstract: Systems and methods are shown for providing private local sponsored content selection and improving intelligence models through distribution among mobile devices. This allows greater data gathering capabilities through the use of the sensors of the mobile devices as well as data stored on data storage components of the mobile devices to create predicted models while offering better opportunities to preserve privacy. Locally stored profiles comprising machine intelligence models may also be used to determine the relevance of the data gathered and in improving an aggregated model for identifying the relevance of data and the selection of sponsored content items. Distributed optimization is used in conjunction with privacy techniques to create the improved machine intelligence models. Publishers may also benefit from the improved privacy by protecting the statistics of type or volume of sponsored content items shown with publisher content.
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公开(公告)号：US20210042787A1
公开(公告)日：2021-02-11
申请号：US16698548
申请日：2019-11-27
Applicant: Google LLC
Inventor： Michael Kleber , Gang Wang , Daniel Ramage , Charlie Harrison , Josh Karlin , Moti Yung
IPC: G06Q30/02 , H04L9/32 , H04L9/08 , G06F16/951
Abstract: The present disclosure provides systems and methods for content quasi-personalization or anonymized content retrieval via aggregated browsing history of a large plurality of devices, such as millions or billions of devices. A sparse matrix may be constructed from the aggregated browsing history, and dimensionally reduced, reducing entropy and providing anonymity for individual devices. Relevant content may be selected via quasi-personalized clusters representing similar browsing histories, without exposing individual device details to content providers.
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公开(公告)号：US10901577B2
公开(公告)日：2021-01-26
申请号：US16037418
申请日：2018-07-17
Applicant: Google LLC
Inventor： Tim Wantland , Julian Odell , Seungyeon Kim , Iulia Turc , Daniel Ramage , Wei Huang , Kaikai Wang
IPC: G06F17/00 , G06F3/0482 , G06F3/0484
Abstract: The present disclosure is directed to input suggestion. In particular, the methods and systems of the present disclosure can: receive, from a first application executed by one or more computing devices, data indicating information that has been presented by and/or input into the first application; generate, based at least in part on the received data, one or more suggested candidate inputs for a second application executed by the computing device(s); provide, in association with the second application, an interface comprising one or more options to select at least one suggested candidate input of the suggested candidate input(s); and responsive to receiving data indicating a selection of a particular suggested candidate input of the suggested candidate input(s) via the interface, communicate, to the second application, data indicating the particular suggested candidate input.
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公开(公告)号：US20200026395A1
公开(公告)日：2020-01-23
申请号：US16037418
申请日：2018-07-17
Applicant: Google LLC
Inventor： Tim Wantland , Julian Odell , Seungyeon Kim , Iulia Turc , Daniel Ramage , Wei Huang , Kaikai Wang
IPC: G06F3/0482 , G06F3/0484
Abstract: The present disclosure is directed to input suggestion. In particular, the methods and systems of the present disclosure can: receive, from a first application executed by one or more computing devices, data indicating information that has been presented by and/or input into the first application; generate, based at least in part on the received data, one or more suggested candidate inputs for a second application executed by the computing device(s); provide, in association with the second application, an interface comprising one or more options to select at least one suggested candidate input of the suggested candidate input(s); and responsive to receiving data indicating a selection of a particular suggested candidate input of the suggested candidate input(s) via the interface, communicate, to the second application, data indicating the particular suggested candidate input.
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