Best Papers
 Accelerating ResNet Inference on RNS-CKKS with Merged Bootstrapping 


Vol. 50,  No. 11, pp. 1669-1678, Nov.  2025
10.7840/kics.2025.50.11.1669


PDF Full-Text
  Abstract

Recent studies on convolutional neural networks based on fully homomorphic encryption(FHE) have gained momentum, and there are reports of implementing ResNet with high accuracy on the representative FHE residue number system variant of Cheon–Kim–Kim–Song(RNS-CKKS). However, existing approaches under‑utilize ciphertext slots, requiring frequent bootstrapping and therefore incurring substantial computational overhead. In response to this, our paper proposes a 'merged bootstrapping' method that, when performing residual network(ResNet) on multiple images simultaneously, merges multiple ciphertexts into a single ciphertext before bootstrapping. This effectively utilizes all slots of the ciphertext, significantly reducing computation time. Experimental results using the RNS-CKKS scheme library, Lattigo, confirmed that when classifying 2, 4, and 8 Canadian Institute for Advanced Research-10(CIFAR-10) images with ResNet-20 using merged bootstrapping,

  Statistics
Cumulative Counts from November, 2022
Multiple requests among the same browser session are counted as one view. If you mouse over a chart, the values of data points will be shown.


  Related Articles
  Cite this article

[IEEE Style]

S. Park and E. Lee, "Accelerating ResNet Inference on RNS-CKKS with Merged Bootstrapping," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 11, pp. 1669-1678, 2025. DOI: 10.7840/kics.2025.50.11.1669.

[ACM Style]

Soomin Park and Eunsang Lee. 2025. Accelerating ResNet Inference on RNS-CKKS with Merged Bootstrapping. The Journal of Korean Institute of Communications and Information Sciences, 50, 11, (2025), 1669-1678. DOI: 10.7840/kics.2025.50.11.1669.

[KICS Style]

Soomin Park and Eunsang Lee, "Accelerating ResNet Inference on RNS-CKKS with Merged Bootstrapping," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 11, pp. 1669-1678, 11. 2025. (https://doi.org/10.7840/kics.2025.50.11.1669)
Vol. 50, No. 11 Index

Search
(IN TITLE, AUTHOR, ABSTRACT,KEYWORDS)

Advanced Search ▼

POPULAR KEYWORDS
(TOP 10 KEYWORDS)

Recent Publications
(LAST 3 YEARS)






pISSN : 1226 - 4717
eISSN : 2287 - 3880