公开(公告)号：US20230078704A1

公开(公告)日：2023-03-16

申请号：US17795131

申请日：2021-12-17

Applicant: Google LLC

Inventor： Yunting Sun ,  Marcel M. Moti Yung ,  Gang Wang

IPC: H04L9/08 ,  G06N20/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying labels for a dataset without revealing the dataset to any individual computing system. Methods can include receiving, by a first computing system of a multi-party computation (MPC) system, a query that includes a first and second share of a given user profile. The second share is encrypted with a key that prevents the first computing system from accessing the second share. The second share is transmitted to a second computing system of the MPC system. The first and the second computing system generates a machine learning model and identifies a respective first and a second label. The first computing system receives the second label as a response from the second computing system. The first computing system responds to the query with a response that includes the first and the second label.

公开(公告)号：US10853730B2

公开(公告)日：2020-12-01

申请号：US15704939

申请日：2017-09-14

Applicant: Google LLC

Inventor： Yunting Sun ,  David Chan ,  James Koehler ,  Yuxue Jin ,  Yueqing Wang

IPC: G06N5/02 ,  G06Q30/02

Abstract: Systems, methods, and computer-readable storage media that may be used to generate a category Bayesian hierarchical model. One method includes receiving a brand data set for each of a plurality of brands within a category, each brand data set comprising content input for a particular brand of the plurality of brands for a plurality of media channels at a plurality of times and a response for the particular brand of the plurality of brands at the plurality of times. The method includes determining a plurality of informative priors by generating a category Bayesian hierarchical model based on the plurality of brand data sets and a plurality of weak priors. The method further includes generating a brand Bayesian hierarchical model that models response for the particular brand for each of the plurality of media channels based on the brand data set for the particular brand and the plurality of informative priors.