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.

公开(公告)号：US20220129760A1

公开(公告)日：2022-04-28

申请号：US17511448

申请日：2021-10-26

Applicant: Google LLC

Inventor： Shanmugasundaram Ravikumar ,  Badih Ghazi ,  Pasin Manurangsi ,  Chiyuan Zhang ,  Noah Golowich

IPC: G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training neural networks with label differential privacy. One of the methods includes, for each training example: processing the network input in the training example using the neural network in accordance with the values of the network parameters as of the beginning of the training iteration to generate a network output, generating a private network output for the training example from the target output in the training example and the network output for the training example, and generating a modified training example that includes the network input in the training example and the private network output for the training example; and training the neural network on at least the modified training examples to update the values of the network parameters.