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公开(公告)号：US20220100901A1
公开(公告)日：2022-03-31
申请号：US17545819
申请日：2021-12-08
申请人： Lucinity ehf
摘要： A method of providing an auto-encoder for anonymizing data associated with a population of entities is disclosed. The method includes providing a computer system with a memory storing specific computer-executable instructions for a neural network. The neural network includes an input layer of nodes; three or more layers of nodes; and an output layer of nodes to provide an encoded output vector. The second layer of nodes has more nodes than the first and third layers of nodes. The method also includes identifying a plurality of characteristics associated with the entities and preparing a plurality of input vectors that include a characteristic. The characteristics appear in the input vector as transformed numeric information from human recognizable text. The method includes training the neural network during a plurality of training cycles comprising: processing an input vector with the neural network to provide an encoded output vector; determining an output vector reconstruction error by calculating a function of the encoded output vector and the input vector; back-propagating the output vector reconstruction error back through the neural network; and recalibrating a weight to minimize the output vector reconstruction error. Additional neural networks are also disclosed. The outputs of the additional neural networks may be combined. Encoded output vectors may be compared to identify a common characteristic between two or more entities or to identify two or more entities with the common characteristic. An auto-encoder system for anonymizing data is also disclosed.
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公开(公告)号：US20240311511A1
公开(公告)日：2024-09-19
申请号：US18669133
申请日：2024-05-20
申请人： Lucinity ehf
CPC分类号： G06F21/6254 , G06N3/045 , G06N3/048 , G06N3/084
摘要： A method of providing an auto-encoder for anonymizing data associated with a population of entities is disclosed. The method includes providing a computer system with a memory storing specific computer-executable instructions for a neural network. The neural network includes an input layer of nodes; three or more layers of nodes; and an output layer of nodes to provide an encoded output vector. The second layer of nodes has more nodes than the first and third layers of nodes. The method also includes identifying a plurality of characteristics associated with the entities and preparing a plurality of input vectors that include a characteristic. The characteristics appear in the input vector as transformed numeric information from human recognizable text. The method includes training the neural network during a plurality of training cycles comprising: processing an input vector with the neural network to provide an encoded output vector; determining an output vector reconstruction error by calculating a function of the encoded output vector and the input vector; back-propagating the output vector reconstruction error back through the neural network; and recalibrating a weight to minimize the output vector reconstruction error. Additional neural networks are also disclosed. The outputs of the additional neural networks may be combined. Encoded output vectors may be compared to identify a common characteristic between two or more entities or to identify two or more entities with the common characteristic. An auto-encoder system for anonymizing data is also disclosed.
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公开(公告)号：US11989327B2
公开(公告)日：2024-05-21
申请号：US17545819
申请日：2021-12-08
申请人： Lucinity ehf
CPC分类号： G06F21/6254 , G06N3/045 , G06N3/048 , G06N3/084
摘要： A method of providing an auto-encoder for anonymizing data associated with a population of entities is disclosed. The method includes providing a computer system with a memory storing specific computer-executable instructions for a neural network. The neural network includes an input layer of nodes; three or more layers of nodes; and an output layer of nodes to provide an encoded output vector. The second layer of nodes has more nodes than the first and third layers of nodes. The method also includes identifying a plurality of characteristics associated with the entities and preparing a plurality of input vectors that include a characteristic. The characteristics appear in the input vector as transformed numeric information from human recognizable text. The method includes training the neural network during a plurality of training cycles comprising: processing an input vector with the neural network to provide an encoded output vector; determining an output vector reconstruction error by calculating a function of the encoded output vector and the input vector; back-propagating the output vector reconstruction error back through the neural network; and recalibrating a weight to minimize the output vector reconstruction error. Additional neural networks are also disclosed. The outputs of the additional neural networks may be combined. Encoded output vectors may be compared to identify a common characteristic between two or more entities or to identify two or more entities with the common characteristic. An auto-encoder system for anonymizing data is also disclosed.
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公开(公告)号：US20210089682A1
公开(公告)日：2021-03-25
申请号：US17020453
申请日：2020-09-14
申请人： Lucinity ehf
摘要： A method of providing an auto-encoder for anonymizing data associated with a population of entities is disclosed. The method includes providing a computer system with a memory storing specific computer-executable instructions for a neural network. The neural network includes an input layer of nodes; three or more layers of nodes; and an output layer of nodes to provide an encoded output vector. The second layer of nodes has more nodes than the first and third layers of nodes. The method also includes identifying a plurality of characteristics associated with the entities and preparing a plurality of input vectors that include a characteristic. The characteristics appear in the input vector as transformed numeric information from human recognizable text. The method includes training the neural network during a plurality of training cycles comprising: processing an input vector with the neural network to provide an encoded output vector; determining an output vector reconstruction error by calculating a function of the encoded output vector and the input vector; back-propagating the output vector reconstruction error back through the neural network; and recalibrating a weight to minimize the output vector reconstruction error. Additional neural networks are also disclosed. The outputs of the additional neural networks may be combined. Encoded output vectors may be compared to identify a common characteristic between two or more entities or to identify two or more entities with the common characteristic. An auto-encoder system for anonymizing data is also disclosed.
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公开(公告)号：US12045716B2
公开(公告)日：2024-07-23
申请号：US17020496
申请日：2020-09-14
申请人： Lucinity ehf
IPC分类号： G06N3/08 , G06N3/045 , G06Q30/018 , G06Q40/02 , G06Q50/26
CPC分类号： G06N3/08 , G06N3/045 , G06Q30/0185 , G06Q40/02 , G06Q50/265
摘要： A method of updating a first neural network is disclosed. The method includes providing a computer system with a computer-readable memory that stores specific computer-executable instructions for the first neural network and a second neural network separate from the first neural network. The method also includes providing one or more processors in communication with the computer-readable memory. The one or more processors are programmed by the computer-executable instructions to at least process a first data with the first neural network, process a second data with the second neural network, update a weight in a node of the second neural network by a delta amount as a function of the processing of the second data with the second neural network, and update a weight in a node of the first neural network as a function of the delta amount. A computer system for updating a first neural network is also disclosed. Other features of the preferred embodiments are also disclosed.
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公开(公告)号：US11227067B2
公开(公告)日：2022-01-18
申请号：US17020453
申请日：2020-09-14
申请人： Lucinity ehf
摘要： A method of providing an auto-encoder for anonymizing data associated with a population of entities is disclosed. The method includes providing a computer system with a memory storing specific computer-executable instructions for a neural network. The neural network includes an input layer of nodes; three or more layers of nodes; and an output layer of nodes to provide an encoded output vector. The second layer of nodes has more nodes than the first and third layers of nodes. The method also includes identifying a plurality of characteristics associated with the entities and preparing a plurality of input vectors that include a characteristic. The characteristics appear in the input vector as transformed numeric information from human recognizable text. The method includes training the neural network during a plurality of training cycles comprising: processing an input vector with the neural network to provide an encoded output vector; determining an output vector reconstruction error by calculating a function of the encoded output vector and the input vector; back-propagating the output vector reconstruction error back through the neural network; and recalibrating a weight to minimize the output vector reconstruction error. Additional neural networks are also disclosed. The outputs of the additional neural networks may be combined. Encoded output vectors may be compared to identify a common characteristic between two or more entities or to identify two or more entities with the common characteristic. An auto-encoder system for anonymizing data is also disclosed.
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公开(公告)号：US20210089899A1
公开(公告)日：2021-03-25
申请号：US17020496
申请日：2020-09-14
申请人： Lucinity ehf
摘要： A method of updating a first neural network is disclosed. The method includes providing a computer system with a computer-readable memory that stores specific computer-executable instructions for the first neural network and a second neural network separate from the first neural network. The method also includes providing one or more processors in communication with the computer-readable memory. The one or more processors are programmed by the computer-executable instructions to at least process a first data with the first neural network, process a second data with the second neural network, update a weight in a node of the second neural network by a delta amount as a function of the processing of the second data with the second neural network, and update a weight in a node of the first neural network as a function of the delta amount. A computer system for updating a first neural network is also disclosed. Other features of the preferred embodiments are also disclosed.
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