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Theoretical Computer Science

Hardness of (M)LWE with semi-uniform seeds

Wenjuan Jia, Jiang Zhang, B. B. Xiang, Baocang Wang

Published Feb 29, 2024• 1 citation
Publisher

Abstract

Let (D,S,χ,m)-LWEn,q be the LWE problem in matrix form (A,y=As+emodq), where A,s,e are randomly chosen respectively from the seed distribution D over Zqm×n, secret distribution S over Zqn and noise distribution χm over Zm (or Rm), i.e., A←D,s←S,e←χm. For various secret-noise distributions (S,χ), the (D,S,χ,m)-LWEn,q problem is shown to be as hard as some standard worst-case lattice problems, but most of the known results require D to be the uniform distribution over Zqm×n. In this paper, we show that under the standard LWE assumption, the problem (D,S,χ,m)-LWEn,q can still be hard for some distribution D that is not (even computationally indistinguishable from) the uniform distribution over Zqm×n. Specifically, we show that if D is a semi-uniform distribution over Zqm×n (namely, D can be publicly derived from and has a “small difference” to the uniform distribution over Zqm×n), then for appropriate choices of (S,χ), the problem (D,S,χ,m)-LWEn,q is hard under the standard LWE assumption. Moreover, we also show that the semi-uniform MLWE problem is hard under the standard MLWE assumption. As a direct application, our results pave the way to prove the security of public-key encryptions with rounded public keys under the standard (M)LWE assumption.

DOI
10.1016/j.tcs.2024.114481

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Let (D,S,χ,m)-LWEn,q be the LWE problem in matrix form (A,y=As+emodq), where A,s,e are randomly chosen respectively from the seed distribution D over Zqm×n, secret distribution S over Zqn and noise distribution χm over Zm (or Rm), i.e., A←D,s←S,e←χm. For various secret-noise distributions (S,χ), the (D,S,χ,m)-LWEn,q problem is shown to be as hard as some standard worst-case lattice problems, but most of the known results require D to be the uniform distribution over Zqm×n. In this paper, we show that under the standard LWE assumption, the problem (D,S,χ,m)-LWEn,q can still be hard for some distribution D that is not (even computationally indistinguishable from) the uniform distribution over Zqm×n. Specifically, we show that if D is a semi-uniform distribution over Zqm×n (namely, D can be publicly derived from and has a “small difference” to the uniform distribution over Zqm×n), then for appropriate choices of (S,χ), the problem (D,S,χ,m)-LWEn,q is hard under the standard LWE assumption. Moreover, we also show that the semi-uniform MLWE problem is hard under the standard MLWE assumption. As a direct application, our results pave the way to prove the security of public-key encryptions with rounded public keys under the standard (M)LWE assumption.
In certain designs and applications of practical lattice-based cryptography, the use of a specialized variant of LWE problems, where the public matrix is sampled from a non-uniform distribution, is required to establish the security of the corresponding cryptographic schemes. Recently, the formal definition of LWE problems with semi-uniform seeds was introduced by the community, in which the hardness of Euclidean, ideal, and module lattice-based LWE problems with semi-uniform seeds was proved through reduction approach similar to those employed in the hardness proofs of entropic LWE problems. However, known reduction introduces significant losses in the Gaussian error parameters and lattice dimensions. Moreover, additional non-standard assumptions are required to demonstrate the hardness of LWE problems with semi-uniform seeds over rings. In this paper, a tighter reduction is proposed for LWE problems with semi-uniform seeds by incorporating modified techniques from the hardness proofs of Hint-LWE problems. The proposed reduction is unaffected by the algebraic structure of the underlying problems and can be uniformly applied to Euclidean, ideal, and module lattice-based LWE problems with semi-uniform seeds. The hardness of these LWE problems can be established based on standard LWE assumptions without the need for any additional non-standard assumption. Furthermore, the dimension of corresponding LWE problems remains unchanged, and the reduction introduces only minimal losses in Gaussian error parameters.
The module learning with errors (MLWE) problem has attracted considerable attention for its tradeoff between security and efficiency. The quantum/classical worst‐case to average‐case hardness for the MLWE problem (or more exactly, a family of problems) has been established, but most of the known results require the seed distribution to be the uniform distribution. In the present paper, we show that, using the noise flooding technique based on the Rényi divergence, the search MLWE problem with uniform B ‐bounded secret distribution for 1 ≤ B ≪ q can still be hard for some seed distributions that are not (even computationally indistinguishable from) the uniform distribution under the standard MLWE assumption. Specifically, we show that if the seed distribution is a semiuniform distribution (namely, the seed distribution can be publicly derived from and has a “small difference” to the uniform distribution), then for suitable parameter choices, the search MLWE problem with uniform bounded secret distribution is hard under the standard MLWE assumption. Moreover, we also show that under the appropriate setting of parameters, the search MLWE problem with uniform bounded noise distribution is at least as hard as the standard MLWE assumption using a different approach than the one used by Boudgoust et al. in [JoC 2023].
Abstract. In this paper we propose the first provably secure public key encryption scheme based on the Learning with Errors (LWE) problem, in which secrets and errors are sampled uniformly at random from a relatively small set rather than from the commonly used discrete Gaussian distribution. Using a uniform distribution, instead of a Gaussian, has the potential of improving computational efficiency a great deal due to its simplicity, thus making the scheme attractive for use in practice. At the same time our scheme features the strong security guarantee of being based on the hardness of worst-case lattice problems. After presenting the construction of our scheme we prove its security and propose asymptotic parameters. Finally, we compare our scheme on several measures to one of the most efficient LWE-based encryption schemes with Gaussian noise. We show that the expected efficiency improvement is debunked, due to the large blow-up of the parameter sets involved.
In this paper we propose the first provably secure public key encryption scheme based on the Learning with Errors (LWE) problem, in which secrets and errors are sampled uniformly at random from a relatively small set rather than from the commonly used discrete Gaussian distribution. Using a uniform distribution, instead of a Gaussian, has the potential of improving computational efficiency a great deal due to its simplicity, thus making the scheme attractive for use in practice. At the same time our scheme features the strong security guarantee of being based on the hardness of worst-case lattice problems. After presenting the construction of our scheme we prove its security and propose asymptotic parameters. Finally, we compare our scheme on several measures to one of the most efficient LWE-based encryption schemes with Gaussian noise. We show that the expected efficiency improvement is debunked, due to the large blow-up of the parameter sets involved.
In this paper we propose the first provably secure public key encryption scheme based on the Learning with Errors (LWE) problem, in which secrets and errors are sampled uniformly at random from a relatively small set rather than from the commonly used discrete Gaussian distribution.Using a uniform distribution, instead of a Gaussian, has the potential of improving computational efficiency a great deal due to its simplicity, thus making the scheme attractive for use in practice.At the same time our scheme features the strong security guarantee of being based on the hardness of worst-case lattice problems.After presenting the construction of our scheme we prove its security and propose asymptotic parameters.Finally, we compare our scheme on several measures to one of the most efficient LWE-based encryption schemes with Gaussian noise.We show that the expected efficiency improvement is debunked, due to the large blow-up of the parameter sets involved.
The module learning with errors (MLWE) problem has attracted significant attention and has been widely used in building a multitude of lattice‐based cryptographic primitives. The hardness of the MLWE problem has been established for several variants, but most of the known results require the seed distribution (i.e., the distribution of matrix A ) to be the uniform distribution. In this paper, we show that under the Module‐N‐th degree Truncated polynomial Ring Units (NTRU) (MNTRU) assumption, the search MLWE problem can still be hard for some distributions that are not (even computationally indistinguishable from) the uniform distribution. Specifically, we show that if the seed distribution is a semiuniform distribution (namely, the seed distribution can be publicly derived from and has a “small difference” to the uniform distribution), then for appropriate settings of parameters, the search MLWE problem is hard under the MNTRU assumption. Moreover, we also show that under the appropriate settings of parameters, the search learning with errors over rings problem with semiuniform seeds can still be hard under the NTRU assumption due to our results for the search MLWE problem with semiuniform seeds being rank‐preserving.