中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

28 records (took 0.02 seconds)

    Training Machine-Learned Models with Label Differential Privacy
    13.
    发明公开
    Training Machine-Learned Models with Label Differential Privacy 审中-公开

    公开(公告)号：US20240265294A1

    公开(公告)日：2024-08-08

    申请号：US18156915

    申请日：2023-01-19

    Applicant: Google LLC

    Inventor： Badih Ghazi ,  Pritish Kamath ,  Shanmugasundaram Ravikumar ,  Ethan Jacob Leeman ,  Pasin Manurangsi ,  Avinash Vaidyanathan Varadarajan ,  Chiyuan Zhang

    IPC: G06N20/00

    CPC classification number: G06N20/00

    Abstract: An example method is provided for conducting differentially private communication of training data for training a machine-learned model. Initial label data can be obtained that corresponds to feature data. A plurality of label bins can be determined to respectively provide representative values for initial label values assigned to the plurality of label bins. Noised label data can be generated, based on a probability distribution over the plurality of label bins, to correspond to the initial label data, the probability distribution characterized by, for a respective noised label corresponding to a respective initial label of the initial label data, a first probability for returning a representative value of a label bin to which the respective initial label is assigned, and a second probability for returning another value. The noised label data can be communicated for training the machine-learned model.

    ADAPTIVE PRIVACY-PRESERVING INFORMATION RETRIEVAL
    14.
    发明申请
    ADAPTIVE PRIVACY-PRESERVING INFORMATION RETRIEVAL 有权

    公开(公告)号：US20250139282A1

    公开(公告)日：2025-05-01

    申请号：US17926281

    申请日：2022-08-23

    Applicant: Google LLC

    Inventor： Pasin Manurangsi ,  Shanmugasundaram Ravikumar ,  Badih Ghazi ,  Matthew Tran Clegg ,  Joseph Sean Cahill Goodknight Knightbrook

    IPC: G06F21/62

    Abstract: Methods, systems, and apparatus, including medium-encoded computer program products, for adaptive privacy-preserving information retrieval. An information server can accept from a user a request for privacy sensitive information accessible to the information server. The information server can determine a remaining privacy allocation for the user of the information server and can determine a noise parameter for a response to the request, where application of the noise parameter to the response can decrease a privacy loss associated with the response. The information server can determine a privacy modifier for the response. In response to the information server determining that the remaining privacy allocation satisfies the privacy modifier, the information server can: (i) determining the response to the request; (ii) apply the noise parameter to the response to produce a noised response; (iii) provide the noised response to the user; and (iv) adjust the remaining privacy allocation according to the privacy modifier.

    PRIVACY SENSITIVE ESTIMATION OF DIGITAL RESOURCE ACCESS FREQUENCY
    15.
    发明申请
    PRIVACY SENSITIVE ESTIMATION OF DIGITAL RESOURCE ACCESS FREQUENCY 有权

    公开(公告)号：US20250131112A1

    公开(公告)日：2025-04-24

    申请号：US18684996

    申请日：2023-07-14

    Applicant: Google LLC

    Inventor： Pasin Manurangsi ,  Badih Ghazi ,  Shanmugasundaram Ravikumar ,  Jelani Osei Nelson

    IPC: G06F21/62

    Abstract: In one aspect, there is provided a method performed by one or more computers that includes: obtaining access data for a digital resource, access data including data identifying a set of users that accessed the digital resource at a time point, processing the access data to generate data defining a tree model, where each node in the tree model is associated with: (i) a key that specifies time intervals in the time span, and (ii) a value that is based on a respective number of users that satisfy a node-specific selection, receiving a request to determine a number of users that accessed the digital resource at least a predefined number of times within a time window, and in processing the tree model to generate an estimate for the number of users that accessed the digital resource at least the predefined number of times within the time window.

    Pure differentially private algorithms for summation in the shuffled model
    18.
    发明授权
    Pure differentially private algorithms for summation in the shuffled model 有权

    公开(公告)号：US11902259B2

    公开(公告)日：2024-02-13

    申请号：US17122638

    申请日：2020-12-15

    Applicant: Google LLC

    Inventor： Badih Ghazi ,  Noah Zeger Golowich ,  Shanmugasundaram Ravikumar ,  Pasin Manurangsi ,  Ameya Avinash Velingker ,  Rasmus Pagh

    IPC: H04L29/06 ,  H04L9/40 ,  G06N5/04 ,  G06N20/00 ,  H04L9/06 ,  H04L9/08

    CPC classification number: H04L63/0428 ,  G06N5/04 ,  G06N20/00

    Abstract: An encoding method for enabling privacy-preserving aggregation of private data can include obtaining private data including a private value, determining a probabilistic status defining one of a first condition and a second condition, producing a multiset including a plurality of multiset values, and providing the multiset for aggregation with a plurality of additional multisets respectively generated for a plurality of additional private values. In response to the probabilistic status having the first condition, the plurality of multiset values is based at least in part on the private value, and in response to the probabilistic status having the second condition, the plurality of multiset values is a noise message. The noise message is produced based at least in part on a noise distribution that comprises a discretization of a continuous unimodal distribution supported on a range from zero to a number of multiset values included in the plurality of multiset values.

    DETERMINING STRENGTH OF ASSOCIATION BETWEEN USER CONTACTS
    19.
    发明申请
    DETERMINING STRENGTH OF ASSOCIATION BETWEEN USER CONTACTS 有权

    公开(公告)号：US20220377037A1

    公开(公告)日：2022-11-24

    申请号：US17882238

    申请日：2022-08-05

    Applicant: GOOGLE LLC

    Inventor： Andrew Tomkins ,  Shanmugasundaram Ravikumar ,  Shalini Agarwal ,  MyLinh Yang ,  Bo Pang ,  Mark Yinan Li

    IPC: H04L51/216 ,  G06F16/28 ,  H04L51/52

    Abstract: Methods and apparatus related to identifying one or more messages sent by a user, identifying two or more contacts that are associated with one or more of the messages, determining a strength of relationship score between identified contacts, and utilizing the strength of relationship scores to provide additional information related to the contacts. A strength of relationship score between a contact and one or more other contacts may be determined based on one or more properties of one or more of the messages. In some implementations, contacts groups may be determined based on the strength of relationship scores. In some implementations, contacts groups may be utilized to disambiguate references to contacts in messages. In some implementations, contacts group may be utilized to provide suggestions to the user of additional contacts of a contacts group that includes the indicated recipient contact of a message.

    Systems and Methods for Clustering with List-Decodable Covers
    20.
    发明申请
    Systems and Methods for Clustering with List-Decodable Covers 有权

    公开(公告)号：US20220300558A1

    公开(公告)日：2022-09-22

    申请号：US17204546

    申请日：2021-03-17

    Applicant: Google LLC

    Inventor： Shanmugasundaram Ravikumar ,  Pasin Manurangsi ,  Badih Ghazi

    IPC: G06F16/906 ,  G06K9/62

    Abstract: Example techniques are provided for the task of differentially private clustering. For several basic clustering problems, including Euclidean DensestBall, 1-Cluster, k-means, and k-median, the present disclosure provides efficient differentially private algorithms that achieve essentially the same approximation ratios as those that can be obtained by any non-private algorithm, while incurring only small additive errors. This improves upon existing efficient algorithms that only achieve some large constant approximation factors.

Patent Agency Ranking