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Middle-Product Learning with Rounding Problem and Its Applications

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Advances in Cryptology – ASIACRYPT 2019 (ASIACRYPT 2019)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11921))

Abstract

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.

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Notes

  1. 1.

    This can be done by sampling \(s \leftarrow U\left( {\mathbb Z}_q^{<n+d+k-1} {[x]} \right) \) uniformly at random and rejecting it if its Hankel matrix is not full rank.

  2. 2.

    The full version of this paper can be found in the Cryptology ePrint Archive with link: https://eprint.iacr.org/2019/1001.

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Acknowledgments

This work is supported by the European Union PROMETHEUS project (Horizon 2020 Research and Innovation Program, grant 780701). This work has also received a French government support managed by the National Research Agency in the “Investing for the Future” program, under the national project RISQ P141580-2660001/DOS0044216, and under the project TYREX granted by the CominLabs excellence laboratory with reference ANR-10-LABX-07-01. This work is also supported through NATO SPS Project G5448 and through NIST awards 60NANB18D216 and 60NANB18D217.

Katharina Boudgoust is funded by the Direction Générale de l’Armement (Pôle de Recherche CYBER). Dipayan Das is funded by MHRD, India.

We also thank our anonymous referees for their helpful and constructive comments.

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Authors and Affiliations

  1. Department of Mathematical Sciences, Florida Atlantic University, Boca Raton, USA

    Shi Bai

  2. Univ Rennes, CNRS, IRISA, Rennes, France

    Katharina Boudgoust, Adeline Roux-Langlois & Weiqiang Wen

  3. Department of Mathematics, National Institute of Technology, Durgapur, India

    Dipayan Das

  4. Algorand, Boston, USA

    Zhenfei Zhang

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  1. Shi Bai
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Correspondence to Shi Bai .

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  1. University of Auckland, Auckland, New Zealand

    Steven D. Galbraith

  2. Security Fundamentals Lab, NICT, Tokyo, Japan

    Shiho Moriai

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Bai, S., Boudgoust, K., Das, D., Roux-Langlois, A., Wen, W., Zhang, Z. (2019). Middle-Product Learning with Rounding Problem and Its Applications. In: Galbraith, S., Moriai, S. (eds) Advances in Cryptology – ASIACRYPT 2019. ASIACRYPT 2019. Lecture Notes in Computer Science(), vol 11921. Springer, Cham. https://doi.org/10.1007/978-3-030-34578-5_3

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