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Optimized layerwise approximation for efficient private inference on fully homomorphic encryption

delete2026-05-19
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PRE
AI
J
Junghyun Lee
J
Joon-Woo Lee
E
Eunsang Lee
Y
Young Sik Kim
Y
Yongwoo Lee
Y
Yongjune Kim *
J
Jong‐Seon No
DOI:10.1016/j.neucom.2026.134016delete
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Abstract

Abstract

En 中文
• Fully homomorphic encryption strongly protects sensitive data. • Fully homomorphic encryption requires approximations for non-arithmetic operations. • Post-training approximation eliminates the need for retraining the pre-trained model. • Evaluating high-degree polynomials on ciphertexts incurs significant time latency. • Optimized layerwise approximation reduces time latency for private inference.
Keywords:
Fully homomorphic encryption
Private inference
Layerwise approximation
Time latency
Non-arithmetic operations

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Neurocomputing cover
Neurocomputing
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6.5
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2.5W
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6.5W

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