公开(公告)号：US20240185032A1

公开(公告)日：2024-06-06

申请号：US18505492

申请日：2023-11-09

Applicant: Google LLC

Inventor： Martin Abadi ,  David Godbe Andersen

IPC: G06N3/045 ,  G06N3/084

CPC classification number: G06N3/045 ,  G06N3/084

Abstract: Systems, methods, devices, and other techniques are described herein for training and using neural networks to encode inputs and to process encoded inputs, e.g., to reconstruct inputs from the encoded inputs. A neural network system can include an encoder neural network, a trusted decoder neural network, and an adversary decoder neural network. The encoder neural network processes a primary neural network input and a key input to generate an encoded representation of the primary neural network input. The trusted decoder neural network processes the encoded representation and the key input to generate a first estimated reconstruction of the primary neural network input. The adversary decoder neural network processes the encoded representation without the key input to generate a second estimated reconstruction of the primary neural network input. The encoder and trusted decoder neural networks can be trained jointly, and these networks trained adversarially to the adversary decoder neural network.

公开(公告)号：US11853860B2

公开(公告)日：2023-12-26

申请号：US17685559

申请日：2022-03-03

Applicant: Google LLC

Inventor： Martin Abadi ,  David Godbe Andersen

IPC: G06N3/045 ,  G06N3/084

CPC classification number: G06N3/045 ,  G06N3/084

Abstract: Systems, methods, devices, and other techniques are described herein for training and using neural networks to encode inputs and to process encoded inputs, e.g., to reconstruct inputs from the encoded inputs. A neural network system can include an encoder neural network, a trusted decoder neural network, and an adversary decoder neural network. The encoder neural network processes a primary neural network input and a key input to generate an encoded representation of the primary neural network input. The trusted decoder neural network processes the encoded representation and the key input to generate a first estimated reconstruction of the primary neural network input. The adversary decoder neural network processes the encoded representation without the key input to generate a second estimated reconstruction of the primary neural network input. The encoder and trusted decoder neural networks can be trained jointly, and these networks trained adversarially to the adversary decoder neural network.

公开(公告)号：US20230019228A1

公开(公告)日：2023-01-19

申请号：US17685559

申请日：2022-03-03

Applicant: Google LLC

Inventor： Martin Abadi ,  David Godbe Andersen

IPC: G06N3/04 ,  G06N3/08

Abstract: Systems, methods, devices, and other techniques are described herein for training and using neural networks to encode inputs and to process encoded inputs, e.g., to reconstruct inputs from the encoded inputs. A neural network system can include an encoder neural network, a trusted decoder neural network, and an adversary decoder neural network. The encoder neural network processes a primary neural network input and a key input to generate an encoded representation of the primary neural network input. The trusted decoder neural network processes the encoded representation and the key input to generate a first estimated reconstruction of the primary neural network input. The adversary decoder neural network processes the encoded representation without the key input to generate a second estimated reconstruction of the primary neural network input. The encoder and trusted decoder neural networks can be trained jointly, and these networks trained adversarially to the adversary decoder neural network.

公开(公告)号：US11308385B2

公开(公告)日：2022-04-19

申请号：US16323205

申请日：2017-08-03

Applicant: Google LLC

Inventor： Martin Abadi ,  David Godbe Andersen

IPC: G06N3/04 ,  G06N3/08

Abstract: Systems, methods, devices, and other techniques are described herein for training and using neural networks to encode inputs and to process encoded inputs, e.g., to reconstruct inputs from the encoded inputs. A neural network system can include an encoder neural network, a trusted decoder neural network, and an adversary decoder neural network. The encoder neural network processes a primary neural network input and a key input to generate an encoded representation of the primary neural network input. The trusted decoder neural network processes the encoded representation and the key input to generate a first estimated reconstruction of the primary neural network input. The adversary decoder neural network processes the encoded representation without the key input to generate a second estimated reconstruction of the primary neural network input. The encoder and trusted decoder neural networks can be trained jointly, and these networks trained adversarially to the adversary decoder neural network.

公开(公告)号：US20190171929A1

公开(公告)日：2019-06-06

申请号：US16323205

申请日：2017-08-03

Applicant: Google LLC

Inventor： Martin Abadi ,  David Godbe Andersen

IPC: G06N3/04 ,  G06N3/08

Abstract: Systems, methods, devices, and other techniques are described herein for training and using neural networks to encode inputs and to process encoded inputs, e.g., to reconstruct inputs from the encoded inputs. A neural network system can include an encoder neural network, a trusted decoder neural network, and an adversary decoder neural network. The encoder neural network processes a primary neural network input and a key input to generate an encoded representation of the primary neural network input. The trusted decoder neural network processes the encoded representation and the key input to generate a first estimated reconstruction of the primary neural network input. The adversary decoder neural network processes the encoded representation without the key input to generate a second estimated reconstruction of the primary neural network input. The encoder and trusted decoder neural networks can be trained jointly, and these networks trained adversarially to the adversary decoder neural network.

公开(公告)号：US10044718B2

公开(公告)日：2018-08-07

申请号：US14824727

申请日：2015-08-12

Applicant: Google LLC

Inventor： Michael Burrows ,  Martin Abadi ,  Himabindu Pucha ,  Adam Sadovsky ,  Asim Shankar ,  Ankur Taly

IPC: H04L29/06 ,  G06F21/60 ,  G06F21/62

Abstract: In a method of controlling sharing of an object between entities in a distributed system, a processor will identify an object and generate an access control list (ACL) for the object so that the ACL includes a list of clauses. Each clause will include a blessing pattern that will match one or more blessings, and at least one of the clauses also may include a reference to one or more groups. Each group represents a set of strings that represent blessing patterns or fragments of blessing patterns. The processor may generate each clause of the ACL as either a permit clause or a deny clause to indicate whether an entity or entities that have a blessing matched by the blessing pattern are permitted to access the object. The processor will save the ACL to a data store for use in responding to a request to access the object.