Paper 2025/1942
Privacy-Preserving Shape Matching with Leveled Homomorphic Encryption
Abstract
Homomorphic Encryption (HE) allows parties to securely outsource data while enabling computation on encrypted data, protect- ing against malicious parties and data leakages. More recent HE schemes enable approximate arithmetic on complex vectors and approximation of non-linear functions, specifically useful for image processing algorithms. The Fourier Shape Descriptor (FSD) is a classical method for shape matching via frequency-domain representation, and we show that FSD can be computed entirely in the encrypted domain. To the best of our knowledge, this is the first work to implement secure shape descriptors and matching via HE. We also present two experiments for similar and different shapes, and measure the performance of the encrypted algo- rithm.
Metadata
- Available format(s)
-
PDF
- Category
- Applications
- Publication info
- Preprint.
- Keywords
- CryptographyHomomorphic EncryptionFourier DescriptorPrivacy-PreservingEncrypted Computation
- Contact author(s)
-
agayevagha @ gmail com
yadigar imamverdiyev @ aztu edu az - History
- 2025-10-20: approved
- 2025-10-17: received
- See all versions
- Short URL
- https://ia.cr/2025/1942
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2025/1942,
author = {Agha Aghayev and Yadigar Imamverdiyev},
title = {Privacy-Preserving Shape Matching with Leveled Homomorphic Encryption},
howpublished = {Cryptology {ePrint} Archive, Paper 2025/1942},
year = {2025},
url = {https://eprint.iacr.org/2025/1942}
}
