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Related Concept Videos

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Reconstruction of Signal using Interpolation

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Related Experiment Videos

Efficient Non-Interactive Discrete ReLU over CKKS Using Interpolation Look-Up Table.

Zhigang Chen1, Xinxia Song2, Liqun Chen3

  • 1College of Artificial Intelligence, Ningbo University of Finance and Economics, Ningbo 315175, China.

Entropy (Basel, Switzerland)
|May 26, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces novel methods for evaluating discrete ReLU activations on encrypted data using the CKKS homomorphic encryption scheme. These techniques offer competitive accuracy for encrypted neural network inference with manageable storage requirements.

Keywords:
CKKS schemeReLU functionfully homomorphic encryptionfunctional bootstrappinginterpolation-based lookup table

Related Experiment Videos

Area of Science:

  • Cryptography
  • Machine Learning
  • Computer Science

Background:

  • Evaluating nonlinear activations like ReLU on encrypted data is crucial for privacy-preserving machine learning.
  • The CKKS homomorphic encryption scheme efficiently handles packed arithmetic but struggles with direct nonlinear function evaluation due to approximate number semantics.
  • Polynomial approximations for ReLU in CKKS can lead to approximation errors and non-discrete outputs.

Purpose of the Study:

  • To develop efficient, task-specific methods for discrete ReLU evaluation within the CKKS homomorphic encryption scheme without data decryption.
  • To address the limitations of polynomial approximations in CKKS for nonlinear activations.

Main Methods:

  • A novel construction combining modulus-switch-based discretization with interpolation-driven lookup-table (LUT) evaluation for discrete ReLU in CKKS.
  • Two complementary schemes: one using trigonometric Hermite interpolation and functional bootstrapping for a discrete sign indicator, and another using iterative MSB bootstrapping for higher precision.
  • A discretization step to map approximate CKKS plaintexts to a finite integer representation, enabling exact evaluation over this discrete domain.

Main Results:

  • The proposed schemes achieve competitive accuracy for encrypted MNIST inference compared to polynomial-approximation baselines.
  • The methods maintain manageable auxiliary storage, making them suitable for practical applications.
  • Demonstrated the effectiveness of interpolation-based discrete activation as an alternative to polynomial approximation in CKKS.

Conclusions:

  • Interpolation-based discrete activation is a viable and promising alternative to polynomial approximation for specific CKKS-based encrypted inference tasks.
  • The developed schemes provide efficient and accurate solutions for evaluating discrete ReLU on encrypted data.
  • These advancements contribute to the field of privacy-preserving machine learning through homomorphic encryption.