Pseudorandom functions (PRFs) are a very important tool in cryptography, and the learning with rounding (LWR) problem is one of the main issues in their construction. LWR problem, is to find from ⌊ A s ⌋ p , where and is the rounding function. The LWR problem is considered a variant of the learning with error (LWE) problem, that is, to find s from b = A s + e , where , and LWE has been reduced to GapSVP and SIVP. The hardness of the lattice problems is the security foundation of the issued schemes. The best‐known reduction for LWR was completed using information‐theoretic entropy arguments, and the reduction requires q ≥ 2 n m p . It does not directly reduce to the closest vector problem (CVP) problem, but rather to the LWE problem. However, the reduction in the aforementioned work significantly reduces the difficulty of LWR. To more accurately characterize the hardness of LWR, this paper uses statistical approximation and a Quantum Fourier Transform to reduce LWR to the CVP, thereby ensuring the hardness of LWR. Furthermore, unlike the previous conclusions, our reduction involves minimal loss and has broad security conditions, requiring only that , where q and p are prime numbers and 0 < α < 1.
Pseudorandom functions (PRFs) are a very important tool in cryptography, and the learning with rounding (LWR) problem is one of the main issues in their construction. LWR problem, is to find from ⌊ A s ⌋ p , where and is the rounding function. The LWR problem is considered a variant of the learning with error (LWE) problem, that is, to find s from b = A s + e , where , and LWE has been reduced to GapSVP and SIVP. The hardness of the lattice problems is the security foundation of the issued schemes. The best‐known reduction for LWR was completed using information‐theoretic entropy arguments, and the reduction requires q ≥ 2 n m p . It does not directly reduce to the closest vector problem (CVP) problem, but rather to the LWE problem. However, the reduction in the aforementioned work significantly reduces the difficulty of LWR. To more accurately characterize the hardness of LWR, this paper uses statistical approximation and a Quantum Fourier Transform to reduce LWR to the CVP, thereby ensuring the hardness of LWR. Furthermore, unlike the previous conclusions, our reduction involves minimal loss and has broad security conditions, requiring only that , where q and p are prime numbers and 0 < α < 1.
Abstract. The learning with rounding (LWR) problem, introduced by Banerjee, Peikert and Rosen [BPR12] at EUROCRYPT ’12, is a variant of learning with errors (LWE), where one replaces random errors with deterministic rounding. The LWR problem was shown to be as hard as LWE for a setting of parameters where the modulus and modulus-to-error ratio are super-polynomial. In this work we resolve the main open problem of [BPR12] and give a new reduction that works for a larger range of parameters, allowing for a polynomial modulus and modulus-to-error ratio. In particular, a smaller modulus gives us greater efficiency, and a smaller modulus-to-error ratio gives us greater security, which now follows from the worst-case hardness of GapSVP with polynomial (rather than super-polynomial) approximation factors. As a tool in the reduction, we show that there is a “lossy mode ” for the LWR problem, in which LWR samples only reveal partial information about the secret. This property gives us several interesting new applications, including a proof that LWR remains secure with weakly random secrets of sufficient minentropy, and very simple new constructions of deterministic encryption, lossy trapdoor functions and reusable extractors. Our approach is inspired by a technique of Goldwasser et al. [GKPV10] from ICS ’10, which implicitly showed the existence of a “lossy mode ” for LWE. By refining this technique, we also improve on the parameters of that work to only requiring a polynomial (instead of super-polynomial) modulus and modulus-to-error ratio.
The Learning with Rounding (LWR) problem, introduced as a deterministic variant of Learning with Errors (LWE), has become a promising foundation for post-quantum cryptography. This Systematization of Knowledge (SoK) article presents a comprehensive survey of the theoretical foundations, algorithmic developments, and practical implementations of LWR-based cryptographic schemes. We introduce LWR within the broader landscape of lattice-based cryptography and post-quantum security, highlighting its advantages such as reduced randomness, improved efficiency, and enhanced side-channel resistance. We explore the evolution of security reductions from LWR to LWE, including recent advances that support practical parameter regimes and address challenges in both bounded and unbounded sample settings. This article systematically reviews existing LWR-based schemes — including Saber, Lizard, Florete, Espada, Sable, and SMAUG — analyzing their design choices, parameter sets, and performance tradeoffs. Furthermore, we examine the impact of LWR on side-channel resistance, failure probabilities, and masking efficiency, demonstrating its suitability for secure and efficient implementations. By consolidating the research spanning theory and practice, this SoK aims at guiding future cryptographic design and standardization efforts leveraging LWR.
We show the following reductions from the learning with errors problem (LWE) to the learning with rounding problem (LWR): (1) Learning the secret and (2) distinguishing samples from random strings is at least as hard for LWR as it is for LWE for efficient algorithms if the number of samples is no larger than O(q/Bp), where q is the LWR modulus, p is the rounding modulus, and the noise is sampled from any distribution supported over the set {−B,..., B}. Our second result generalizes a theorem of Alwen, Krenn, Pietrzak, and Wichs (CRYPTO 2013) and provides an alternate proof of it. Unlike Alwen et al., we do not impose any number theoretic restrictions on the modulus q. The first result also extends to variants of LWR and LWE over polynomial rings. As additional results we show that (3) distinguishing any number of LWR samples from random strings is of equivalent hardness to LWE whose noise distribution is uniform over the integers in the range [−q/2p,..., q/2p) provided q is a multiple of p and (4) the “noise flooding” technique for converting faulty LWE noise to a discrete Gaussian distribution can be applied whenever q = Ω(B
Our main result is a reduction from worst-case lattice problems such as GapSVP and SIVP to a certain learning problem. This learning problem is a natural extension of the “learning from parity with error” problem to higher moduli. It can also be viewed as the problem of decoding from a random linear code. This, we believe, gives a strong indication that these problems are hard. Our reduction, however, is quantum. Hence, an efficient solution to the learning problem implies a quantum algorithm for GapSVP and SIVP. A main open question is whether this reduction can be made classical (i.e., nonquantum). We also present a (classical) public-key cryptosystem whose security is based on the hardness of the learning problem. By the main result, its security is also based on the worst-case quantum hardness of GapSVP and SIVP. The new cryptosystem is much more efficient than previous lattice-based cryptosystems: the public key is of size Õ( n 2 ) and encrypting a message increases its size by a factor of Õ( n ) (in previous cryptosystems these values are Õ( n 4 ) and Õ( n 2 ), respectively). In fact, under the assumption that all parties share a random bit string of length Õ( n 2 ), the size of the public key can be reduced to Õ( n ).
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.
Our main result is a reduction from worst-case lattice problems such as GapSVP and SIVP to a certain learning problem. This learning problem is a natural extension of the `learning from parity with error' problem to higher moduli. It can also be viewed as the problem of decoding from a random linear code. This, we believe, gives a strong indication that these problems are hard. Our reduction, however, is quantum. Hence, an efficient solution to the learning problem implies a quantum algorithm for GapSVP and SIVP. A main open question is whether this reduction can be made classical (i.e., non-quantum). We also present a (classical) public-key cryptosystem whose security is based on the hardness of the learning problem. By the main result, its security is also based on the worst-case quantum hardness of GapSVP and SIVP. The new cryptosystem is much more efficient than previous lattice-based cryptosystems: the public key is of size O~(n2) and encrypting a message increases its size by a factor of O~(n) (in previous cryptosystems these values are O~(n4) and O~(n2), respectively). In fact, under the assumption that all parties share a random bit string of length O~(n2), the size of the public key can be reduced to O~(n).