Paper 2025/791
Analysis of One Privacy-Preserving Electricity Data Classification Scheme Based on CNN Model With Fully Homomorphism
Abstract
We show that the data classification scheme [IEEE Trans. Sustain. Comput., 2023, 8(4), 652-669)] failed to check the compatibility of encoding algorithm and homomorphic encryption algorithm. Some calculations should be revised to ensure all operands are first encoded using the same scaling factors. The canonical embedding map depending on the natural projection should be explicitly arranged so as to construct an efficient decoding algorithm.
Metadata
- Available format(s)
-
PDF
- Category
- Attacks and cryptanalysis
- Publication info
- Preprint.
- Keywords
- Homomorphic encryptiondata classificationscaling factorscanonical embedding mapnatural projection
- Contact author(s)
- liulh @ shmtu edu cn
- History
- 2025-05-05: approved
- 2025-05-04: received
- See all versions
- Short URL
- https://ia.cr/2025/791
- License
-
CC0
BibTeX
@misc{cryptoeprint:2025/791,
author = {Zhengjun Cao and Lihua Liu},
title = {Analysis of One Privacy-Preserving Electricity Data Classification Scheme Based on {CNN} Model With Fully Homomorphism},
howpublished = {Cryptology {ePrint} Archive, Paper 2025/791},
year = {2025},
url = {https://eprint.iacr.org/2025/791}
}
