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Security and Communication Networks

LWR-Based Fully Homomorphic Encryption, Revisited

Fucai Luo, Fuqun Wang, Kunpeng Wang, Jie Li, Kefei Chen

Published Jan 1, 2018• 17 citations
Publisher

Abstract

Very recently, Costache and Smart proposed a fully homomorphic encryption (FHE) scheme based on the Learning with Rounding (LWR) problem, which removes the noise (typically, Gaussian noise) sampling needed in the previous lattices-based FHEs. But their scheme did not work, since the noise of homomorphic multiplication is complicated and large, which leads to failure of decryption. More specifically, they chose LWR instances as a public key and the private key therein as a secret key and then used the tensor product to implement homomorphic multiplication, which resulted in a tangly modulus problem. Recall that there are two moduli in the LWR instances, and then the moduli will tangle together due to the tensor product. Inspired by their work, we built the first workable LWR-based FHE scheme eliminating the tangly modulus problem by cleverly adopting the celebrated approximate eigenvector method proposed by Gentry et al. at Crypto 2013. Roughly speaking, we use a specific matrix multiplication to perform the homomorphic multiplication, hence no tangly modulus problem. Furthermore, we also extend the LWR-based FHE scheme to the multikey setting using the tricks used to construct LWE-based multikey FHE by Mukherjee and Wichs at Eurocrypt 2016. Our LWR-based multikey FHE construction provides an alternative to the existing multikey FHEs and can also be applied to multiparty computation with higher efficiency.

DOI
10.1155/2018/5967635

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Very recently, Costache and Smart proposed a fully homomorphic encryption (FHE) scheme based on the Learning with Rounding (LWR) problem, which removes the noise (typically, Gaussian noise) sampling needed in the previous lattices-based FHEs. But their scheme did not work, since the noise of homomorphic multiplication is complicated and large, which leads to failure of decryption. More specifically, they chose LWR instances as a public key and the private key therein as a secret key and then used the tensor product to implement homomorphic multiplication, which resulted in a tangly modulus problem. Recall that there are two moduli in the LWR instances, and then the moduli will tangle together due to the tensor product. Inspired by their work, we built the first workable LWR-based FHE scheme eliminating the tangly modulus problem by cleverly adopting the celebrated approximate eigenvector method proposed by Gentry et al. at Crypto 2013. Roughly speaking, we use a specific matrix multiplication to perform the homomorphic multiplication, hence no tangly modulus problem. Furthermore, we also extend the LWR-based FHE scheme to the multikey setting using the tricks used to construct LWE-based multikey FHE by Mukherjee and Wichs at Eurocrypt 2016. Our LWR-based multikey FHE construction provides an alternative to the existing multikey FHEs and can also be applied to multiparty computation with higher efficiency.
Almost all existing well‐known fully homomorphic encryption (FHE) schemes, which are based on either the learning with errors (LWE) or the ring LWE problem, require expensive Gaussian noise sampling. In this study, the authors propose an FHE scheme based on the ring learning with rounding (RLWR) problem. The learning with rounding (LWR) problem was proposed as a deterministic variant of LWE, while the RLWR is a variant of LWR. Sampling an LWR instance does not require Gaussian noise sampling process, and neither does an RLWR instance. Thus, our FHE scheme can be instantiated without the need for Gaussian noise sampling. To implement homomorphic operations, we devise a specific relinearisation method. Furthermore, we also prove that our RLWR‐based FHE scheme is IND‐CPA secure under RLWR assumption.
Fully dynamic multi-key fully homomorphic encryption (FHE) that allows an unlimited number of homomorphic operations for unconstrained parties. That is to say, it supports performing as many computational procedures on inputs (which are encrypted by an unrestricted number of parties) as needed. The existed fully dynamic multi-key FHE scheme is based on the learning with errors (LWE) problem. However, the LWE problem suffers from the intricate and laborious Gaussian sampling which extremely weakens the schemes’ efficiencies. What’s more, it has been lately revealed that Gaussian sampling creates lots of possible side-channel vulnerabilities that result in the disclosure of secret keys. The paper proposed a fully dynamic multi-key FHE that based on the learning with rounding (LWR) problem which leaves out the time-consuming Gaussian sampling required in the LWE problem while sustaining almost the same security level.
The Learning with Errors problem (LWE) and its variants are among the most popular assumptions underlying lattice-based cryptography. The Learning with Rounding problem (LWR) can be thought of as a deterministic variant of LWE. In this work, we present a thorough study of Somewhat Homomorphic Encryption schemes based on Ring-LWR that are the analogue of the Ring-LWE-based BFV scheme. Our main contribution is to present two new schemes, in the LPR and Regev paradigms, and give a thorough analysis of their security (provable and concrete). The technical tools we developed in the process may be of independent interest to the community. Our schemes inherit the many benefits of being based on LWR, including avoiding the need for expensive Gaussian sampling and improved ciphertext size. Indeed, we give a detailed comparison showing that our schemes marginally outperform the BFV scheme in terms of ciphertext size. Moreover, we show that both our schemes support RNS variants. Our Regev-type scheme can be seen as an improved generalisation of the only prior work in this direction (Costache-Smart, 2017). In particular, our scheme resolves the tangled modulus issue in the Costache-Smart proposal that led to unmanageable noise growth, and achieves a factor n improvement in the size of the public key.
Abstract: In this paper, we first introduce a new concept of approximate lattice problem (ALP), which is an extension of learning with errors (LWE). Next, we propose two ALP-based public key encryption schemes. Then, we construct two new fully homomorphic encryption scheme (FHE) based on respectively approximate principal ideal lattice problem with related modulus (APIP-RM) and approximate lattice problem with related modulus (ALP-RM). Moreover, we also extend our ALP-RM-based FHE to the ALP problem with unrelated modulus (ALP-UM). Our work is different from previous works in three aspects: (1) We extend the LWE problem to the ALP problem. This ALP problem is similar to the closest vector problem in lattice. We believe that this problem is independent of interest. (2) We construct a new FHE by using a re-randomizing method, which is different from the squashing decryption in previous works. (3) The expansion rate is merely O(k) with k a security parameter in Our FHE, which can be improved to O(logk) by using dimension reduction [BV11], whereas all previous schemes are at least O(k*logk) [BV11, Gen11, LNV11]. Our method can also decrease a factor k of the expansion rate in their schemes.
In this paper, we first introduce a new concept of approximate lattice problem (ALP), which is an extension of learning with errors (LWE). Next, we propose two ALP-based public key encryption schemes. Then, we construct two new fully homomorphic encryption scheme (FHE) based on respectively approximate principal ideal lattice problem with related modulus (APIP-RM) and approximate lattice problem with related modulus (ALP-RM). Moreover, we also extend our ALP-RM-based FHE to the ALP problem with unrelated modulus (ALP-UM). Our work is different from previous works in three aspects: (1) We extend the LWE problem to the ALP problem. This ALP problem is similar to the closest vector problem in lattice. We believe that this problem is independent of interest. (2) We construct a new FHE by using a re-randomizing method, which is different from the squashing decryption in previous works. (3) The expansion rate is merely O(k) with k a security parameter in Our FHE, which can be improved to O(logk) by using dimension reduction [BV11], whereas all previous schemes are at least O(k*logk) [BV11, Gen11, LNV11]. Our method can also decrease a factor k of the expansion rate in their schemes.
Fully homomorphic encryption (FHE) scheme allows for performing computations on encrypted data without decrypting it which makes it more popular in various domains. Most previous FHE constructions are limited by the large ciphertext expansion rate and error growth rate. Learning with errors (LWE) problem is a popular primitive for constructing lattice-based FHE. Many algebraic variants of LWE are also developed with further promising cryptographic features. Most previous known LWE variants (over the commutative base rings) are unified into a general framework by Peikert et al. at TCC 2019. In 2022, a new algebraically structured LWE problem over the d-degree cyclic algebra (CLWE) (with modulus q), a typical non-commutative ring, was proposed by Grover et al.. To further explore the utility and potential advantages of this new variant, it is interesting to design new lattice-based FHE schemes by using CLWE. In this paper, by following the diagrams of Gentry-Sahai-Waters (GSW13 for short), we propose a FHE scheme based on the CLWE with an even small ciphertext size. Due to our further expansion of the plaintext space, the average ciphertext expansion rate (CER for short) is smaller. More precisely, compared to original GSW13 and the related GSW-style constructions, the ciphertext size of our proposal is reduced by almost at least 23.69%, and the average CER is reduced about 96% due to our expansion of the plaintext space. Besides, our FHE has an even small error growth rate (EGR for short). After one time multiplication of two ciphertexts, the asymptotical EGR is reduced about 49.8% compared to the original GSW13 and the related GSW-style constructions. Typically, the smaller ciphertext size, CER and EGR make our FHE scheme more efficient in trusted computing environment.