International Association
for Cryptologic Research

Plonk is a widely used succinct non-interactive proof system that uses univariate polynomial commitments. Plonk is quite flexible: it supports circuits with low-degree ``custom'' gates as well as circuits with lookup gates (a lookup gates ensures that its input is contained in a predefined table). For large circuits, the bottleneck in generating a Plonk proof is the need for computing a large FFT. We present HyperPlonk, an adaptation of Plonk to the boolean hypercube, using multilinear polynomial commitments. HyperPlonk retains the flexibility of Plonk, but provides a number of additional benefits. First, it avoids the need for an FFT during proof generation. Second, and more importantly, it supports custom gates of much higher degree than Plonk, without harming the running time of the prover. Both of these can dramatically speed-up the prover's running time. Since HyperPlonk relies on multilinear polynomial commitments, we revisit two elegant constructions: one from Orion and one from Virgo. We show how to reduce the Orion opening proof size to less than 10kb (an almost factor 1000 improvement), and show how to make the Virgo FRI-based opening proof simpler and shorter.

We describe a digital signature scheme $$mathsf {MPSign}$$ , whose security relies on the conjectured hardness of the Polynomial Learning With Errors problem ( $$mathsf {PLWE}$$ ) for at least one defining polynomial within an exponential-size family (as a function of the security parameter). The proposed signature scheme follows the Fiat-Shamir framework and can be viewed as the Learning With Errors counterpart of the signature scheme described by Lyubashevsky at Asiacrypt 2016, whose security relies on the conjectured hardness of the Polynomial Short Integer Solution ( $$mathsf {PSIS}$$ ) problem for at least one defining polynomial within an exponential-size family. As opposed to the latter, $$mathsf {MPSign}$$ enjoys a security proof from $$mathsf {PLWE}$$ that is tight in the quantum-access random oracle model. The main ingredient is a reduction from $$mathsf {PLWE}$$ for an arbitrary defining polynomial among exponentially many, to a variant of the Middle-Product Learning with Errors problem ( $$mathsf {MPLWE}$$ ) that allows for secrets that are small compared to the working modulus. We present concrete parameters for $$mathsf {MPSign}$$ using such small secrets, and show that they lead to significant savings in signature length over Lyubashevsky’s Asiacrypt 2016 scheme (which uses larger secrets) at typical security levels. As an additional small contribution, and in contrast to $$mathsf {MPSign}$$ (or $$mathsf {MPLWE}$$ ), we present an efficient key-recovery attack against Lyubashevsky’s scheme (or the inhomogeneous $$mathsf {PSIS}$$ problem), when it is used with sufficiently small secrets, showing the necessity of a lower bound on secret size for the security of that scheme.

We provide new zero-knowledge argument of knowledge systems that work directly for a wide class of language, namely, ones involving the satisfiability of matrix-vector relations and integer relations commonly found in constructions of lattice-based cryptography. Prior to this work, practical arguments for lattice-based relations either have a constant soundness error $$(2/3)$$, or consider a weaker form of soundness, namely, extraction only guarantees that the prover is in possession of a witness that “approximates” the actual witness. Our systems do not suffer from these limitations.The core of our new argument systems is an efficient zero-knowledge argument of knowledge of a solution to a system of linear equations, where variables of this solution satisfy a set of quadratic constraints. This argument enjoys standard soundness, a small soundness error $$(1/poly)$$, and a complexity linear in the size of the solution. Using our core argument system, we construct highly efficient argument systems for a variety of statements relevant to lattices, including linear equations with short solutions and matrix-vector relations with hidden matrices.Based on our argument systems, we present several new constructions of common privacy-preserving primitives in the standard lattice setting, including a group signature, a ring signature, an electronic cash system, and a range proof protocol. Our new constructions are one to three orders of magnitude more efficient than the state of the art (in standard lattice). This illustrates the efficiency and expressiveness of our argument system.

At CRYPTO 2017, Roşca et al. introduce a new variant of the Learning With Errors (LWE) problem, called the Middle-Product LWE ( $${\mathrm {MP}\text {-}\mathrm{LWE}}$$ ). The hardness of this new assumption is based on the hardness of the Polynomial LWE (P-LWE) problem parameterized by a set of polynomials, making it more secure against the possible weakness of a single defining polynomial. As a cryptographic application, they also provide an encryption scheme based on the $${\mathrm {MP}\text {-}\mathrm{LWE}}$$ problem. In this paper, we propose a deterministic variant of their encryption scheme, which does not need Gaussian sampling and is thus simpler than the original one. Still, it has the same quasi-optimal asymptotic key and ciphertext sizes. The main ingredient for this purpose is the Learning With Rounding (LWR) problem which has already been used to derandomize LWE type encryption. The hardness of our scheme is based on a new assumption called Middle-Product Computational Learning With Rounding, an adaption of the computational LWR problem over rings, introduced by Chen et al. at ASIACRYPT 2018. We prove that this new assumption is as hard as the decisional version of MP-LWE and thus benefits from worst-case to average-case hardness guarantees.

If q is a prime and n is a positive integer then any two finite fields of order $$q^n$$qn are isomorphic. Elements of these fields can be thought of as polynomials with coefficients chosen modulo q, and a notion of length can be associated to these polynomials. A non-trivial isomorphism between the fields, in general, does not preserve this length, and a short element in one field will usually have an image in the other field with coefficients appearing to be randomly and uniformly distributed modulo q. This key feature allows us to create a new family of cryptographic constructions based on the difficulty of recovering a secret isomorphism between two finite fields. In this paper we describe a fully homomorphic encryption scheme based on this new hard problem.

In this paper, we propose a new assumption, the Computational Learning With Rounding over rings, which is inspired by the computational Diffie-Hellman problem. Assuming the hardness of R-LWE, we prove this problem is hard when the secret is small, uniform and invertible. From a theoretical point of view, we give examples of a key exchange scheme and a public key encryption scheme, and prove the worst-case hardness for both schemes with the help of a random oracle. Our result improves both speed, as a result of not requiring Gaussian secret or noise, and size, as a result of rounding. In practice, our result suggests that decisional R-LWR based schemes, such as Saber, Round2 and Lizard, which are among the most efficient solutions to the NIST post-quantum cryptography competition, stem from a provable secure design. There are no hardness results on the decisional R-LWR with polynomial modulus prior to this work, to the best of our knowledge.