Invention Application
- Patent Title: HOMOMORPHIC ENCRYPTION FOR MACHINE LEARNING AND NEURAL NETWORKS USING HIGH-THROUGHPUT CRT EVALUATION
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Application No.: US17833498Application Date: 2022-06-06
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Publication No.: US20220321321A1Publication Date: 2022-10-06
- Inventor: Santosh Ghosh , Andrew Reinders , Rafael Misoczki , Rosario Cammarota , Manoj Sastry
- Applicant: Intel Corporation
- Applicant Address: US CA Santa Clara
- Assignee: Intel Corporation
- Current Assignee: Intel Corporation
- Current Assignee Address: US CA Santa Clara
- Main IPC: H04L9/00
- IPC: H04L9/00 ; G06F7/72 ; G06N3/08 ; H04L9/06

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 by performing a reverse Chinese remainder theorem (CRT) transform on the results of multiplying the decomposed coefficients.
Public/Granted literature
- US11777707B2 Homomorphic encryption for machine learning and neural networks using high-throughput CRT evaluation Public/Granted day:2023-10-03
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