Invention Grant
- 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.: US11777707B2Publication Date: 2023-10-03
- 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
- Agency: JAFFERY WATSON MENDONSA & HAMILTON LLP
- Main IPC: H04L9/00
- IPC: H04L9/00 ; H04L9/06 ; G06F7/72 ; G09C1/00 ; G06F21/72 ; G06F7/487 ; G06F21/60 ; G06N3/063 ; G06N20/00 ; G06N3/08

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
- US20220321321A1 HOMOMORPHIC ENCRYPTION FOR MACHINE LEARNING AND NEURAL NETWORKS USING HIGH-THROUGHPUT CRT EVALUATION Public/Granted day:2022-10-06
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