SECRET EQUALITY DETERMINATION SYSTEM, SECRET EQUALITY DETERMINATION METHOD AND SECRET EQUALITY DETERMINATION PROGRAM RECORDING MEDIUM

    公开(公告)号:US20210176252A1

    公开(公告)日:2021-06-10

    申请号:US16769792

    申请日:2017-12-05

    Abstract: A random number generation server device includes a random number generation unit generating random numbers, a share addition unit generating secret shared data masked using random numbers and the secret shared data of operands in secret equality determination, a secret shared data generation unit generating secret shared data of inputted values, a secret shared data restoration unit obtaining the original values by restoring the secret shared data, and a determination bit-conjunction unit using the secret shared data to perform secret equality determination. A mask value restoration server device includes a secret shared data generation unit, a secret shared data restoration unit, and a determination bit-conjunction unit. A secure computation server device includes a secret shared data generation unit, a secret shared data restoration unit, and a determination bit-conjunction unit.

    SERVER DEVICE, SECRET EQUALITY DETERMINATION SYSTEM, SECRET EQUALITY DETERMINATION METHOD AND SECRET EQUALITY DETERMINATION PROGRAM RECORDING MEDIUM

    公开(公告)号:US20200374107A1

    公开(公告)日:2020-11-26

    申请号:US16769697

    申请日:2017-12-05

    Abstract: A server device, a secret equality determination system, a secret equality determination method and a secret equality determination program recording medium are provided which, regardless of the server sharing scheme, can run with no difference in the number of communication rounds, whether carried out with a ring of order 2 or with a ring of an order greater than 2. This server device is provided with a secret shared data generation unit, a data storage unit, a mask unit, a random number share bit-conjunction unit, a random number share generation unit, a determination bit-conjunction unit and a secret shared data restoration unit. The secret shared data generation unit generates secret shared data. The data storage unit stores the secret shared data. The mask unit uses random number secret shared data to mask certain shared data. The random number share generation unit generates random number shares in which random numbers are secretly shared. In parallel with other calculations, the random number share bit-conjunction unit calculates the logical product of the values in which the random numbers are secretly shared. The determination bit-conjunction unit performs a secret equality determination using the value outputted by the random number share bit-conjunction unit.

    LEARNING APPARATUS, INFERENCE APPARATUS, LEARNING METHOD, AND COMPUTER-READABLE MEDIUM

    公开(公告)号:US20240378499A1

    公开(公告)日:2024-11-14

    申请号:US18559389

    申请日:2021-05-13

    Abstract: A learning apparatus according to the present example embodiment includes: a data dividing unit that generates n sets of divided data by dividing first learning data into n (n is an integer of 2 or more); an inference device generation unit that generates n inference devices for learning data generation by machine learning using data excluding one set of divided data from the first learning data; a learning data generation unit that generates second learning data by inputting the one set of the divided data excluded from the machine learning into each of the n inference devices for learning data generation; and a learning unit that generates a second inference device by machine learning using the second learning data.

    SECURE COMPUTATION SYSTEM, SECURE COMPUTATION SERVERAPPARATUS, SECURE COMPUTATION METHOD, AND SECURECOMPUTATION PROGRAM

    公开(公告)号:US20240007274A1

    公开(公告)日:2024-01-04

    申请号:US18247055

    申请日:2020-09-29

    Inventor: Hikaru TSUCHIDA

    CPC classification number: H04L9/085 H04L9/0869

    Abstract: A secure computation system comprises at least three secure computation server apparatuses connected to each other via a network, and each of secure computation server apparatuses comprises: a random number generation part that generates a random number for masking an input value; an m-1 bit comparison part that compares a value obtained by removing the most significant bit from input value masked with random number with a value obtained by removing the most significant bit from random number; a carry correction part that corrects calculation of a value obtained by removing the most significant bit from input value on basis of result of comparison; and a most significant bit extraction part that extracts the most significant bit of input value by subtracting corrected value of value obtained by removing the most significant bit from input value from input value.

    SECURE COMPUTATION SYSTEM, SECURE COMPUTATION SERVER APPARATUS, SECURE COMPUTATION METHOD, AND SECURE COMPUTATION PROGRAM

    公开(公告)号:US20230403143A1

    公开(公告)日:2023-12-14

    申请号:US18035867

    申请日:2020-11-20

    CPC classification number: H04L9/085 G06F21/57

    Abstract: A secure computation system comprising secure computation server apparatuses, each of which comprises: a discriminant share generation part that computes discriminant shares configured so that an index relating to an input corresponds to a specific value from shares representing the index relating to the input and possible combinations of index shares of an array; a combination configuration part that configures a combination of shares of an element in the array and the discriminant shares for all possible combinations of indices of the array; a shuffle part that shuffles the combinations; a reconstruction part that reconstructs the discriminant shares in the shuffled combinations; and a selection part that selects shares of an element in the array in the combinations where the reconstructed value is the specific value.

    MACHINE LEARNING METHOD, MACHINE LEARNING SYSTEM, AND PROGRAM

    公开(公告)号:US20230214482A1

    公开(公告)日:2023-07-06

    申请号:US17922037

    申请日:2020-05-13

    CPC classification number: G06F21/55

    Abstract: The invention includes a first learning phase that a machine learning is performed using first dataset to create a learning model f; and a second learning phase that the first or a second dataset is randomly selected; a result by inputting the first and the second datasets to the learning model f is inputted to a discriminator h having a parameter ω; a machine learning is performed using a result and a ground truth data to train the parameter ω, the result being obtained by having the discriminator h discriminate whether the input data belongs to the first or the second dataset; and when the first dataset is selected, a result by inputting the data of the first dataset to the learning model f is inputted to a defender u to train a parameter τ thereof by using an output of the discriminator h through the defender u.

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