Welcome to the resource topic for 2025/1942
Title:
Privacy-Preserving Shape Matching with Leveled Homomorphic Encryption
Authors: Agha Aghayev, Yadigar Imamverdiyev
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.
ePrint: https://eprint.iacr.org/2025/1942
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