Nektarios Georgios Tsoutsos
, University of Delaware
Fully Homomorphic Encryption (FHE) enables private data processing on untrusted servers. However, FHE performance remains a critical bottleneck for applications like machine learning, which heavily rely on both non-linear operations (e.g., comparisons) and numerous ciphertext-ciphertext (CxC) and ciphertext-plaintext (CxP) multiplications. While modern FHE schemes like TFHE efficiently handle non-linear operations, their multiplication time complexity remains a significant performance limitation. This paper introduces novel algorithms for CxC and CxP multiplication, as well as the dot-product, a critical kernel in machine learning inference (e.g., convolution). Our method leverages Vertical Packing, decomposing multiplication into a series of efficient, precision-dependent lookup table operations. We evaluate our algorithms and their parallelized variants against the default implementation in TFHE-rs and a recent state-of-the-art work. Our results demonstrate several times faster execution time, significantly accelerating FHE for practical applications.
BibTeX
@misc{cryptoeprint:2026/810,
author = {Rostin Shokri and Nektarios Georgios Tsoutsos},
title = {Decomposing Multiplication: A Vertical Packing Approach for Faster {TFHE}},
howpublished = {Cryptology {ePrint} Archive, Paper 2026/810},
year = {2026},
url = {https://eprint.iacr.org/2026/810}
}
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