Paper 2025/899
Improved Noise Bound in BFV Homomorphic Encryption and Its Application to Multiplication
Abstract
Fully Homomorphic Encryption (FHE) enables computations on encrypted data without requiring decryption. However, each computation increases the noise level, which can eventually cause decryption failures once a certain threshold is reached. In particular, homomorphic multiplication significantly amplifies noise in the ciphertext. In this work, we revisit Ring-learning-With-Error (RLWE) based encryption proposed by Fan et al. and present an optimized noise growth approach by swapping the sample space for secret key and error distribution. Thereafter, we revisit BFV homomorphic multiplication proposed by Kim et al. (ASIACRYPT'21) and present an optimized noise bound. Later, we empirically check the hardness of proposed scheme using lattice estimator. Our analysis demonstrates that the proposed method achieves more than 128-bit security and achieves a lower noise bound than existing techniques.
Metadata
- Available format(s)
-
PDF
- Category
- Foundations
- Publication info
- Published elsewhere. Minor revision. ACNS Workshops
- Keywords
- BFVFully Homomorphic Encryption (FHE)MultiplicationNoise BoundRLWE.
- Contact author(s)
-
akshit aggarwal @ iiitg ac in
kelvin li @ deakin edu au
srinibas @ iiitg ac in - History
- 2025-05-21: approved
- 2025-05-20: received
- See all versions
- Short URL
- https://ia.cr/2025/899
- License
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CC BY
BibTeX
@misc{cryptoeprint:2025/899,
author = {Akshit Aggarwal and Yang Li and Srinibas Swain},
title = {Improved Noise Bound in {BFV} Homomorphic Encryption and Its Application to Multiplication},
howpublished = {Cryptology {ePrint} Archive, Paper 2025/899},
year = {2025},
url = {https://eprint.iacr.org/2025/899}
}
