Efficient FHEW Bootstrapping with Small Evaluation Keys, and Applications to Threshold Homomorphic Encryption
Yongwoo Lee, Daniele Micciancio, Andrey Kim, Rakyong Choi, Maxim Deryabin, Jieun Eom, Donghoon Yoo
Abstract
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 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.
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