The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jun. 02, 2026

Filed:

Jan. 19, 2023
Applicant:

Google Llc, Mountain View, CA (US);

Inventors:

Badih Ghazi, Santa Clara, CA (US);

Pritish Kamath, Mountain View, CA (US);

Shanmugasundaram Ravikumar, Piedmont, CA (US);

Ethan Jacob Leeman, Arlington, MA (US);

Pasin Manurangsi, Bangkok, TH;

Avinash Vaidyanathan Varadarajan, Los Altos, CA (US);

Chiyuan Zhang, Mountain View, CA (US);

Assignee:

GOOGLE LLC, Mountain View, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 20/00 (2019.01);
U.S. Cl.
CPC ...
G06N 20/00 (2019.01);
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.


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