Secure multi-party computation and communication

    公开(公告)号:US11836263B1

    公开(公告)日:2023-12-05

    申请号:US18297545

    申请日:2023-04-07

    Applicant: Lemon Inc.

    CPC classification number: G06F21/62 G06F7/507 H04L9/0869

    Abstract: Protecting data privacy in secure multi-party computation and communication is provided. A method of protecting data privacy includes determining a differential privacy configuration, determining a number of iterations based on a first parameter and a second parameter, and for each of the number of iterations generating a random value and a random noise data; generating a first message and a second message; and performing a transfer based on the first message, the second message, and an input data to output one of the first message and the second message. The method also includes generating a first noise data based on the random noise data in each of the number of iterations, generating a first share based on a first dataset and a second dataset, applying the first noise data to the first share, and constructing a result based on the first share and a second share.

    Fast convolution algorithm for composition determination

    公开(公告)号:US11868497B1

    公开(公告)日:2024-01-09

    申请号:US18297405

    申请日:2023-04-07

    Applicant: Lemon Inc.

    CPC classification number: G06F21/6218 G06F17/15

    Abstract: Differential privacy composition determination in secure computation and communication of a dataset is provided. A method for differential privacy composition determination includes determining a differential privacy configuration that includes a first privacy parameter and a second privacy parameter, determining a privacy loss distribution, and providing a number of composition operations. The method also includes determining a third privacy parameter and a fourth privacy parameter for a differential privacy composition based on the differential privacy configuration, the privacy loss distribution, and the number of composition operations. The method further includes controlling the dataset based on at least one of the third privacy parameter and the fourth privacy parameter.

    Secure computation and communication

    公开(公告)号:US11811920B1

    公开(公告)日:2023-11-07

    申请号:US18297424

    申请日:2023-04-07

    Applicant: Lemon Inc.

    CPC classification number: H04L9/085 G09C1/00

    Abstract: Methods and systems for secure computation and communication are described herein. The method includes transforming identifications of a first dataset using a transforming scheme, dispatching the transformed identifications of the first dataset, receiving identifications of a second dataset, transforming the identifications of the second dataset, dispatching the transformed identifications of the second dataset, receiving a set of identifications, generating a first intersection of the received set of identifications and the transformed identifications of second dataset, and determining a first permutation based on the first intersection. The method also includes performing an oblivious shuffling based on the first permutation and a set of attributions to generate a first share. A size of the first share is the same as a size of the first intersection. The method further includes receiving a second share and constructing a first result based on the first share and the second share.

    Secure multi-party computation and communication

    公开(公告)号:US11983285B1

    公开(公告)日:2024-05-14

    申请号:US18490953

    申请日:2023-10-20

    Applicant: Lemon Inc.

    CPC classification number: G06F21/62 G06F7/507 H04L9/0869

    Abstract: Protecting data privacy in secure multi-party computation and communication is provided. A method of protecting data privacy includes determining a differential privacy configuration, determining a number of iterations based on a first parameter and a second parameter, and for each of the number of iterations generating a random value and a random noise data; generating a first message and a second message; and performing a transfer based on the first message, the second message, and an input data to output one of the first message and the second message. The method also includes generating a first noise data based on the random noise data in each of the number of iterations, generating a first share based on a first dataset and a second dataset, applying the first noise data to the first share, and constructing a result based on the first share and a second share.

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