Paper 2026/1401

A New Framework for Efficient Multivariate Functional Bootstrapping

Abstract

Fully homomorphic encryption (FHE) enables computation on encrypted data without decryption. In TFHE, programmable bootstrapping (PBS) evaluates nonlinear functions through lookup tables (LUTs), but a direct multivariate LUT over a $t$-ary plaintext space has size $t^\ell$. This paper studies LUT compression for multivariate functional bootstrapping via variable separation and additive inner representations. We first apply this approach to non-negative integer division with remainder. For a dividend $m$, a divisor $d$, and $h=\lfloor m/d\rfloor$, we use a logarithmic transformation to decompose bivariate division into two univariate logarithmic PBS calls, one homomorphic subtraction, and one outer exponential PBS call. To handle integer plaintexts, we introduce a rounded logarithmic function $\operatorname{clog}_{B,M}$ and give a sufficient condition on $M$ for exact quotient recovery. The resulting homomorphic division-with-remainder algorithm achieves $\widetilde{O}(1)$ equivalent blind-rotation complexity under theoretically optimal parameters, and also yields frameworks for modular reduction and truncated division. We further prove that every finite function $f:[t]^\ell\to[t]$ can be written as $f(x_1,\ldots,x_\ell)=q\left(\sum_{i=1}^{\ell}p_i(x_i)\right)$, and search for small-span representations using simulated annealing with reheating. Experiments show a 3.6x speedup for division with remainder at $t=64$, and a 1.9x speedup for the Hamming-weight interval function, compared with estimates based on [BBR26].