Homomorphic encryption for machine learning and neural networks using high-throughput CRT evaluation
US11405176B2 · kind B2 · utility
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17Claims
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Key dates
| Filing date | Sep 18, 2020 |
| Grant date | Aug 2, 2022 |
| Priority date | — |
| Expiry date | Sep 26, 2040 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L2209/12
- WIPO fieldDigital communication
- WIPO sectorElectrical engineering
Abstract
Embodiments are directed to homomorphic encryption for machine learning and neural networks using high-throughput Chinese remainder theorem (CRT) evaluation. An embodiment of an apparatus includes a hardware accelerator to receive a ciphertext generated by homomorphic encryption (HE) for evaluation, decompose coefficients of the ciphertext into a set of decomposed coefficients, multiply the decomposed coefficients using a set of smaller modulus determined based on a larger modulus, and convert results of the multiplying back to an original form corresponding to the larger modulus.
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