公开(公告)号：US20250061229A1

公开(公告)日：2025-02-20

申请号：US18938955

申请日：2024-11-06

Applicant: Intel Corporation

Inventor： Micah Sheller ,  Cory Cornelius

IPC: G06F21/62 ,  G06F16/951 ,  G06F18/21 ,  G06F18/2411 ,  G06F21/74 ,  G06N3/04 ,  G06N3/045 ,  G06N3/063 ,  G06N3/08 ,  G06N20/00 ,  G06N99/00 ,  H04L67/10

Abstract: Methods, apparatus, systems and articles of manufacture for distributed use of a machine learning model are disclosed. An example edge device includes a model partitioner to partition a machine learning model received from an aggregator into private layers and public layers. A public model data store is implemented outside of a trusted execution environment of the edge device. The model partitioner is to store the public layers in the public model data store. A private model data store is implemented within the trusted execution environment. The model partitioner is to store the private layers in the private model data store.

公开(公告)号：US20230205918A1

公开(公告)日：2023-06-29

申请号：US18070299

申请日：2022-11-28

Applicant: INTEL CORPORATION

Inventor： Micah Sheller ,  Cory Cornelius

IPC: G06F21/62 ,  H04L67/10 ,  G06N20/00 ,  G06F16/951 ,  G06N3/063 ,  G06F21/74 ,  G06N3/08 ,  G06N99/00 ,  G06N3/04 ,  G06F18/21 ,  G06F18/2411 ,  G06N3/045

CPC classification number: G06F21/6245 ,  H04L67/10 ,  G06N20/00 ,  G06F16/951 ,  G06N3/063 ,  G06F21/74 ,  G06N3/08 ,  G06N99/00 ,  G06N3/04 ,  G06F18/21 ,  G06F18/2411 ,  G06N3/045

Abstract: Methods, apparatus, systems and articles of manufacture for distributed use of a machine learning model are disclosed. An example edge device includes a model partitioner to partition a machine learning model received from an aggregator into private layers and public layers. A public model data store is implemented outside of a trusted execution environment of the edge device. The model partitioner is to store the public layers in the public model data store. A private model data store is implemented within the trusted execution environment. The model partitioner is to store the private layers in the private model data store.

公开(公告)号：US20200327238A1

公开(公告)日：2020-10-15

申请号：US16910722

申请日：2020-06-24

Applicant: INTEL CORPORATION

Inventor： Shih-Han Wang ,  Yonghong Huang ,  Micah Sheller ,  Cory Cornelius

IPC: G06F21/57 ,  G06N5/04 ,  G06F21/56 ,  G06N20/00 ,  G06N3/08 ,  G06F21/55 ,  G06N3/00 ,  G06N3/04

Abstract: Embodiments discussed herein may be generally directed to systems and techniques to generate a quality score based on an observation and an action caused by an actor agent during a testing phase. Embodiments also include determining a temporal difference between the quality score and a previous quality score based on a previous observation and a previous action, determining whether the temporal difference exceeds a threshold value, and generating an attack indication in response to determining the temporal difference exceeds the threshold value.

公开(公告)号：US10726134B2

公开(公告)日：2020-07-28

申请号：US16103137

申请日：2018-08-14

Applicant: INTEL CORPORATION

Inventor： Shih-Han Wang ,  Yonghong Huang ,  Micah Sheller ,  Cory Cornelius

IPC: G06F21/57 ,  G06N5/04 ,  G06F21/56 ,  G06N20/00 ,  G06N3/08 ,  G06F21/55 ,  G06N3/00 ,  G06N3/04

Abstract: Embodiments discussed herein may be generally directed to systems and techniques to generate a quality score based on an observation and an action caused by an actor agent during a testing phase. Embodiments also include determining a temporal difference between the quality score and a previous quality score based on a previous observation and a previous action, determining whether the temporal difference exceeds a threshold value, and generating an attack indication in response to determining the temporal difference exceeds the threshold value.

公开(公告)号：US09450931B2

公开(公告)日：2016-09-20

申请号：US13840572

申请日：2013-03-15

Applicant: Intel Corporation

Inventor： Micah Sheller ,  Conor Cahill ,  Jason Martin ,  Brandon Baker

IPC: G06F7/04 ,  H04L29/06 ,  G06F21/31 ,  G06F21/57

Abstract: Technologies are provided in embodiments to manage an authentication confirmation score. Embodiments are configured to identify, in absolute session time, a beginning time and an ending time of an interval of an active user session on a client. Embodiments are also configured to determine a first value representing a first subset of a set of prior user sessions, where the prior user sessions of the first subset were active for at least as long as the beginning time. Embodiments can also determine a second value representing a second subset of the set of prior user sessions, where the prior user sessions of the second subset were active for at least as long as the ending time. Embodiments also determine, based on the first and second values, a decay rate for the authentication confidence score of the active user session. In some embodiments, the set is based on context attributes.

公开(公告)号：US12169584B2

公开(公告)日：2024-12-17

申请号：US18070299

申请日：2022-11-28

Applicant: INTEL CORPORATION

Inventor： Micah Sheller ,  Cory Cornelius

IPC: G06F21/62 ,  G06F16/951 ,  G06F18/21 ,  G06F18/2411 ,  G06F21/74 ,  G06N3/04 ,  G06N3/045 ,  G06N3/063 ,  G06N3/08 ,  G06N20/00 ,  G06N99/00 ,  H04L67/10

Abstract: Methods, apparatus, systems and articles of manufacture for distributed use of a machine learning model are disclosed. An example edge device includes a model partitioner to partition a machine learning model received from an aggregator into private layers and public layers. A public model data store is implemented outside of a trusted execution environment of the edge device. The model partitioner is to store the public layers in the public model data store. A private model data store is implemented within the trusted execution environment. The model partitioner is to store the private layers in the private model data store.

公开(公告)号：US11657162B2

公开(公告)日：2023-05-23

申请号：US16361397

申请日：2019-03-22

Applicant: Intel Corporation

Inventor： Michael Kounavis ,  Antonios Papadimitriou ,  Anindya Sankar Paul ,  Micah Sheller ,  Li Chen ,  Cory Cornelius ,  Brandon Edwards

IPC: G06F21/60 ,  G06N3/08 ,  G06N3/04 ,  G06F21/52

CPC classification number: G06F21/60 ,  G06F21/52 ,  G06N3/0454 ,  G06N3/08

Abstract: In one example an apparatus comprises a memory and a processor to create, from a first deep neural network (DNN) model, a first plurality of DNN models, generate a first set of adversarial examples that are misclassified by the first plurality of deep neural network (DNN) models, determine a first set of activation path differentials between the first plurality of adversarial examples, generate, from the first set of activation path differentials, at least one composite adversarial example which incorporates at least one intersecting critical path that is shared between at least two adversarial examples in the first set of adversarial examples, and use the at least one composite adversarial example to generate a set of inputs for a subsequent training iteration of the DNN model. Other examples may be described.

公开(公告)号：US11526745B2

公开(公告)日：2022-12-13

申请号：US15892138

申请日：2018-02-08

Applicant: Intel Corporation

Inventor： Micah Sheller ,  Cory Cornelius ,  Jason Martin ,  Yonghong Huang ,  Shih-Han Wang

IPC: G06F21/55 ,  G06N3/08 ,  G06N3/063

Abstract: Methods, apparatus, systems and articles of manufacture for federated training of a neural network using trusted edge devices are disclosed. An example system includes an aggregator device to aggregate model updates provided by one or more edge devices. The one or more edge devices to implement respective neural networks, and provide the model updates to the aggregator device. At least one of the edge devices to implement the neural network within a trusted execution environment.

公开(公告)号：US20190220605A1

公开(公告)日：2019-07-18

申请号：US16361397

申请日：2019-03-22

Applicant: Intel Corporation

Inventor： Michael Kounavis ,  Antonios Papadimitriou ,  Anindya Paul ,  Micah Sheller ,  Li Chen ,  Cory Cornelius ,  Brandon Edwards

IPC: G06F21/60 ,  G06N3/04 ,  G06N3/08

CPC classification number: G06F21/60 ,  G06N3/0454 ,  G06N3/08

Abstract: In one example an apparatus comprises a memory and a processor to create, from a first deep neural network (DNN) model, a first plurality of DNN models, generate a first set of adversarial examples that are misclassified by the first plurality of deep neural network (DNN) models, determine a first set of activation path differentials between the first plurality of adversarial examples, generate, from the first set of activation path differentials, at least one composite adversarial example which incorporates at least one intersecting critical path that is shared between at least two adversarial examples in the first set of adversarial examples, and use the at least one composite adversarial example to generate a set of inputs for a subsequent training iteration of the DNN model. Other examples may be described.

公开(公告)号：US20190042937A1

公开(公告)日：2019-02-07

申请号：US15892138

申请日：2018-02-08

Applicant: Intel Corporation

Inventor： Micah Sheller ,  Cory Cornelius ,  Jason Martin ,  Yonghong Huang ,  Shih-Han Wang

IPC: G06N3/08 ,  G06F21/55

Abstract: Methods, apparatus, systems and articles of manufacture for federated training of a neural network using trusted edge devices are disclosed. An example system includes an aggregator device to aggregate model updates provided by one or more edge devices. The one or more edge devices to implement respective neural networks, and provide the model updates to the aggregator device. At least one of the edge devices to implement the neural network within a trusted execution environment.