SYSTEM FOR HOMOMORPHIC DATA ENCRYPTION BASED ON A SYSTEM OF RESIDUAL CLASSES Russian patent published in 2022 - IPC G06F7/72 H03M7/18 H04L9/08 

Abstract RU 2780150 C1

FIELD: computer technology.

SUBSTANCE: invention relates to computational modular systems and is designed to perform homomorphic data encryption. A system of homomorphic data encryption based on a system of residual classes (SRC), including a data encryption block, a data decryption block, contains n encryption blocks, n blocks for finding residues by modulo pj(x), j=1, ..., n, a computing environment performing calculations on encrypted data, a neural network recovery block, a decryption block, while as the system of residual classes, a polynomial system of residual classes is taken with n mutually simple polynomials pj(x) over the field F2, for which k<n is the threshold, K(x) is the secret key, which is mutually simple with each of the SRC modules, Seed(x) is a random polynomial satisfying the condition where d is the maximum degree of the source data , and N is the number of multiplication operations with encrypted data, the source data comes through encryption blocks to the inputs of the corresponding blocks for finding residues by modulo pj(x), together implementing data encryption according to the formula , where i is the number of the source data, the outputs of the residue finding blocks by modulo pj(x) are connected to the inputs of the computing environment, the outputs of which are connected to the inputs of the neural network recovery block implementing the formula , where , , and P(x) is the product of the modules pj(x), j=1, ..., k, the output of the neural network recovery block connected to the input of the decryption block, which finds the remainder by the secret key K(x), the output of the decryption block is the output of the system.

EFFECT: reduction in the complexity of calculations due to the use of a polynomial system of residual classes and the ability to perform calculations on encrypted data.

1 cl, 2 dwg

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RU 2 780 150 C1

Authors

Babenko Mikhail Grigorevich

Kuchukov Viktor Andreevich

Kucherov Nikolai Nikolaevich

Gladkov Andrei Vladimirovich

Dates

2022-09-19Published

2021-12-27Filed