Offline value transfer using asymmetric cryptography

    公开(公告)号:US11526874B2

    公开(公告)日:2022-12-13

    申请号:US16898977

    申请日:2020-06-11

    Abstract: Tokenized assets with associated value are transferred from a designated server to a mobile device. The associated value is removed from the designated server. The tokenized assets are transferred to a first trusted electronic device. The first trusted electronic device is associated with the mobile device. At least a portion of the tokenized assets are transferred to a second trusted electronic device such that the portion of the tokenized assets are only stored on the second trusted electronic device after the transfer. The second electronic device is associated with a second mobile device. The transfer occurs at a time when both the mobile device and the electronic device are offline.

    Secure erasure of a drive array using drive-defined, trusted computing group bands

    公开(公告)号:US11449265B2

    公开(公告)日:2022-09-20

    申请号:US17084894

    申请日:2020-10-30

    Abstract: Partitions of drives are used to form a volume of a drive array. Each partition is associated with a trusted computing group (TCG) band. Each drive encrypts data stored on the partition with a key unique to the TCG band. The volume is formed using the partitions of the drives. In response to a band-based erasure being invoked on the volume, each drive of the plurality of drives overwrites the key of the TCG band associated with the partition and provides an erasure certificate attesting to the overwriting of the key. The erasure certifications from the drives are compiled into a consolidated erasure certification that attests to the erasure of the volume.

    SECURE ERASURE OF A DRIVE ARRAY USING DRIVE-DEFINED, TRUSTED COMPUTING GROUP BANDS

    公开(公告)号:US20220137850A1

    公开(公告)日:2022-05-05

    申请号:US17084894

    申请日:2020-10-30

    Abstract: Partitions of drives are used to form a volume of a drive array. Each partition is associated with a trusted computing group (TCG) band. Each drive encrypts data stored on the partition with a key unique to the TCG band. The volume is formed using the partitions of the drives. In response to a band-based erasure being invoked on the volume, each drive of the plurality of drives overwrites the key of the TCG band associated with the partition and provides an erasure certificate attesting to the overwriting of the key. The erasure certifications from the drives are compiled into a consolidated erasure certification that attests to the erasure of the volume.

    OFFLINE VALUE TRANSFER USING ASYMMETRIC CRYPTOGRAPHY

    公开(公告)号:US20210390532A1

    公开(公告)日:2021-12-16

    申请号:US16898977

    申请日:2020-06-11

    Abstract: Tokenized assets with associated value are transferred from a designated server to a mobile device. The associated value is removed from the designated server. The tokenized assets are transferred to a first trusted electronic device. The first trusted electronic device is associated with the mobile device. At least a portion of the tokenized assets are transferred to a second trusted electronic device such that the portion of the tokenized assets are only stored on the second trusted electronic device after the transfer. The second electronic device is associated with a second mobile device. The transfer occurs at a time when both the mobile device and the electronic device are offline.

    DISTRIBUTED DECENTRALIZED MACHINE LEARNING MODEL TRAINING

    公开(公告)号:US20210357800A1

    公开(公告)日:2021-11-18

    申请号:US15930776

    申请日:2020-05-13

    Abstract: Systems and methods are disclosed for distributed decentralized machine learning model training. In certain embodiments, a first node in a network may comprise a circuit configured to receive an initial machine learning model having an initial parameter set, apply the local data to update parameters of the initial machine learning model to generate an updated machine learning model, transmit a copy of the updated machine learning model from the first node to a plurality of neighboring nodes in the network via a network interface, receive, via the network interface, a modified machine learning model from a first neighboring node, the modified machine learning model having parameters set based on local data of the first neighboring node, modify the updated machine learning model based on the modified machine learning model, and apply the updated machine learning model to control operations at the first node.

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