International Association
for Cryptologic Research

Recognizing the importance of a fast and resource-efficient polynomial multiplication in homomorphic encryption, in this paper, we design a \emph{multiplier-less} number theoretic transform using a Fermat number as an auxiliary modulus. To make this algorithm scalable with the degree of polynomial, we apply a univariate to multivariate polynomial ring transformation. We develop an accelerator architecture for fully homomorphic encryption using these algorithmic techniques for efficient multivariate polynomial multiplication. For practical homomorphic encryption application benchmarks, the hardware accelerator achieves a 1,200$\times$ speed-up compared to software implementations. Finally, we conclude the paper by discussing the advantages and limitations of the proposed polynomial multiplication method.

There are two competing approaches to bootstrap the FHEW fully homomorphic encryption scheme (Ducas and Micciancio, Eurocrypt 2015) and its variants: the original AP/FHEW method, which supports arbitrary secret key distributions, and the improved GINX/TFHE method, which uses much smaller evaluation keys, but is directly applicable only to binary secret keys, restricting the scheme's applicability. In this paper, we present a new bootstrapping procedure for FHEW-like encryption schemes that achieves the best features of both methods: support for arbitrary secret key distributions at no additional runtime costs, while using small evaluation keys. (Support for arbitrary secret keys is critical in a number of important applications, like threshold and some multi-key homomorphic encryption schemes.) As an added benefit, our new bootstrapping procedure results in smaller noise growth than both AP and GINX, regardless of the key distribution. Our improvements are both theoretically significant (offering asymptotic savings, up to a $O(log n)$ multiplicative factor, either on the running time or public evaluation key size), and practically relevant. For example, for a concrete 128-bit target security level, we show how to decrease the evaluation key size of the best previously known scheme by more than 30\%, while also slightly reducing the running time. We demonstrate the practicality of the proposed methods by building a prototype implementation within the PALISADE/OpenFHE open-source homomorphic encryption library. We provide optimized parameter sets and implementation results showing that the proposed algorithm has the best performance among all known FHEW bootstrapping methods in terms of runtime and key size. We illustrate the benefits of our method by sketching a simple construction of threshold homomorphic encryption based on FHEW.

The promising field of homomorphic encryption enables functions to be evaluated on encrypted data and produce results for the same computations done on plaintexts. It, therefore, comes as no surprise that many ventures at constructing homomorphic encryption schemes have come into the limelight in recent years. Most popular are those that rely on the hard lattice problem, called the Ring Learning with Errors problem (RLWE). One major limitation of these homomorphic encryption schemes is that in order to securely increase the maximum multiplicative depth, they need to increase the polynomial-size (degree of the polynomial ring) thereby also ncreasing the complexity of the design. We aim to bridge this gap by proposing a homomorphic encryption (HE) scheme based on the Module Learning with Errors problem (MLWE), ModHE that allows us to break the big computations into smaller ones. Given the popularity of module lattice-based post-quantum schemes, it is an evidently interesting research endeavor to also formulate module lattice-based homomorphic encryption schemes. While our proposed scheme is general, as a case study, we port the well-known RLWE-based CKKS scheme to the MLWE setting. The module version of the scheme completely stops the polynomial-size blowups when aiming for a greater circuit depth. Additionally, it presents greater opportunities for designing flexible, reusable, and parallelizable hardware architecture. A hardware implementation is provided to support our claims. We also acknowledge that as we try to decrease the complexity of computations, the amount of computations (such as relinearizations) increases. We hope that the potential and limitations of using such a hardware-friendly scheme will spark further research.