公开(公告)号：US20250068972A1

公开(公告)日：2025-02-27

申请号：US18724528

申请日：2022-12-30

Applicant: Google LLC

Inventor： Matthew Royce Markwell ,  Nahid Farhady Ghalaty

IPC: G06N20/00 ,  G06F21/14

Abstract: A computer-implemented method performed on a device comprises receiving input data that describes one or more machine learning (ML) model characteristics of an ML model to be scheduled for execution by the device. The method further comprises determining, based on the one or more ML model characteristics of the ML model, one or more obfuscation instructions to execute concurrently or sequentially with execution of model instructions associated with the ML model. Execution of the one or more obfuscation instructions obfuscates a profile of a measurable parameter associated with the device executing the model instructions. The method further comprises executing the one or more determined obfuscation instructions concurrently or sequentially with execution of the model instructions.

公开(公告)号：US20240185043A1

公开(公告)日：2024-06-06

申请号：US18389010

申请日：2023-11-13

Applicant: Google LLC

Inventor： Jinsung Yoon ,  Michel Jonathan Mizrahi ,  Nahid Farhady Ghalaty ,  Thomas Dunn Henry Jarvinen ,  Ashwin Sura Ravi ,  Peter Robert Brune ,  Fanyu Kong ,  David Roger Anderson ,  George Lee ,  Farhana Bandukwala ,  Eliezer Yosef Kanal ,  Sercan Omer Arik ,  Tomas Pfister

IPC: G06N3/0475 ,  G06N3/0455

CPC classification number: G06N3/0475 ,  G06N3/0455

Abstract: The present disclosure provides a generative modeling framework for generating highly realistic and privacy preserving synthetic records for heterogenous time-series data, such as electronic health record data, financial data, etc. The generative modeling framework is based on a two-stage model that includes sequential encoder-decoder networks and generative adversarial networks (GANs).