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    IDENTIFYING AND AUTHENTICATING USERS BASED ON PASSIVE FACTORS DETERMINED FROM SENSOR DATA
    2.
    发明申请
    IDENTIFYING AND AUTHENTICATING USERS BASED ON PASSIVE FACTORS DETERMINED FROM SENSOR DATA 审中-公开

    公开(公告)号：US20170337364A1

    公开(公告)日：2017-11-23

    申请号：US15600140

    申请日：2017-05-19

    Applicant: UnifyID

    Inventor： John Whaley ,  Kurt W. Somerville

    IPC: G06F21/32 ,  G06N99/00

    CPC classification number: G06N20/00 ,  G06F21/316

    Abstract: The disclosed embodiments relate to a system that authenticates and/or identifies a user of an electronic device based on passive factors, which do not require conscious user actions. During operation of the system, in response to detecting a trigger event, the system collects sensor data from one or more sensors in the electronic device. Next, the system extracts a feature vector from the sensor data. The system then analyzes the feature vector to authenticate and/or identify the user, wherein the feature vector is analyzed using a model trained with sensor data previously obtained from the electronic device while the user was operating the electronic device.

    Privacy-preserving system for machine-learning training data
    3.
    发明授权
    Privacy-preserving system for machine-learning training data 有权

    公开(公告)号：US10601786B2

    公开(公告)日：2020-03-24

    申请号：US15910812

    申请日：2018-03-02

    Applicant: UnifyID

    Inventor： John C. Whaley ,  Eleftherios Ioannidis

    IPC: G06F21/00 ,  H04L29/06 ,  H04L9/30 ,  H04L9/08 ,  G06N20/00 ,  G06F15/76 ,  H04L9/32

    Abstract: The disclosed embodiments relate to a system that anonymizes sensor data to facilitate machine-learning training operations without disclosing an associated user's identity. During operation, the system receives encrypted sensor data at a gateway server, wherein the encrypted sensor data includes a client identifier corresponding to an associated user or client device. Next, the system moves the encrypted sensor data into a secure enclave. The secure enclave then: decrypts the encrypted sensor data; replaces the client identifier with an anonymized identifier to produce anonymized sensor data; and communicates the anonymized sensor data to a machine-learning system. Finally, the machine-learning system: uses the anonymized sensor data to train a model to perform a recognition operation, and uses the trained model to perform the recognition operation on subsequently received sensor data.

    Dummy class framework for continual supervised learning applications
    4.
    发明授权
    Dummy class framework for continual supervised learning applications 有权

    公开(公告)号：US11989268B2

    公开(公告)日：2024-05-21

    申请号：US17174238

    申请日：2021-02-11

    Applicant: UnifyID

    Inventor： Vinay Uday Prabhu ,  John C. Whaley

    IPC: G06F21/31 ,  G06F17/18 ,  G06N3/08

    CPC classification number: G06F21/31 ,  G06F17/18 ,  G06N3/08

    Abstract: The disclosed embodiments provide a system that identifies a user of an electronic device. During a training mode, the system uses an initial training data set, comprising sensor data from electronic devices associated with a set of initial users, to train a multilayer neural network model to authenticate the initial users. Next, the system uses an additional training data set, which includes sensor data from electronic devices associated with one or more new users, to update a portion of the weights in the trained model so that the updated model can be used to authenticate both the initial users and the one or more new users. During a subsequent mode, the system uses the updated model to authenticate a user of the electronic device based on sensor data contemporaneously received from the electronic device.

    DUMMY CLASS FRAMEWORK FOR CONTINUAL SUPERVISED LEARNING APPLICATIONS
    6.
    发明申请
    DUMMY CLASS FRAMEWORK FOR CONTINUAL SUPERVISED LEARNING APPLICATIONS 有权

    公开(公告)号：US20210248215A1

    公开(公告)日：2021-08-12

    申请号：US17174238

    申请日：2021-02-11

    Applicant: UnifyID

    Inventor： Vinay Uday Prabhu ,  John C. Whaley

    IPC: G06F21/31 ,  G06N3/08 ,  G06F17/18

    Abstract: The disclosed embodiments provide a system that identifies a user of an electronic device. During a training mode, the system uses an initial training data set, comprising sensor data from electronic devices associated with a set of initial users, to train a multilayer neural network model to authenticate the initial users. Next, the system uses an additional training data set, which includes sensor data from electronic devices associated with one or more new users, to update a portion of the weights in the trained model so that the updated model can be used to authenticate both the initial users and the one or more new users. During a subsequent surveillance mode, the system uses the updated model to authenticate a user of the electronic device based on sensor data contemporaneously received from the electronic device.

    PRIVACY-PRESERVING SYSTEM FOR MACHINE-LEARNING TRAINING DATA
    7.
    发明申请
    PRIVACY-PRESERVING SYSTEM FOR MACHINE-LEARNING TRAINING DATA 审中-公开

    公开(公告)号：US20180255023A1

    公开(公告)日：2018-09-06

    申请号：US15910812

    申请日：2018-03-02

    Applicant: UnifyID

    Inventor： John C. Whaley ,  Eleftherios Ioannidis

    IPC: H04L29/06 ,  G06F15/18 ,  H04L9/08 ,  H04L9/30

    CPC classification number: H04L63/0421 ,  G06F15/76 ,  G06N20/00 ,  H04L9/0819 ,  H04L9/0825 ,  H04L9/30 ,  H04L9/3231 ,  H04L2209/42

    Abstract: The disclosed embodiments relate to a system that anonymizes sensor data to facilitate machine-learning training operations without disclosing an associated user's identity. During operation, the system receives encrypted sensor data at a gateway server, wherein the encrypted sensor data includes a client identifier corresponding to an associated user or client device. Next, the system moves the encrypted sensor data into a secure enclave. The secure enclave then: decrypts the encrypted sensor data; replaces the client identifier with an anonymized identifier to produce anonymized sensor data; and communicates the anonymized sensor data to a machine-learning system. Finally, the machine-learning system: uses the anonymized sensor data to train a model to perform a recognition operation, and uses the trained model to perform the recognition operation on subsequently received sensor data.

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