2,019 research outputs found
Ring Learning With Errors: A crossroads between postquantum cryptography, machine learning and number theory
The present survey reports on the state of the art of the different
cryptographic functionalities built upon the ring learning with errors problem
and its interplay with several classical problems in algebraic number theory.
The survey is based to a certain extent on an invited course given by the
author at the Basque Center for Applied Mathematics in September 2018.Comment: arXiv admin note: text overlap with arXiv:1508.01375 by other
authors/ comment of the author: quotation has been added to Theorem 5.
Lattice-Based Group Signatures: Achieving Full Dynamicity (and Deniability) with Ease
In this work, we provide the first lattice-based group signature that offers
full dynamicity (i.e., users have the flexibility in joining and leaving the
group), and thus, resolve a prominent open problem posed by previous works.
Moreover, we achieve this non-trivial feat in a relatively simple manner.
Starting with Libert et al.'s fully static construction (Eurocrypt 2016) -
which is arguably the most efficient lattice-based group signature to date, we
introduce simple-but-insightful tweaks that allow to upgrade it directly into
the fully dynamic setting. More startlingly, our scheme even produces slightly
shorter signatures than the former, thanks to an adaptation of a technique
proposed by Ling et al. (PKC 2013), allowing to prove inequalities in
zero-knowledge. Our design approach consists of upgrading Libert et al.'s
static construction (EUROCRYPT 2016) - which is arguably the most efficient
lattice-based group signature to date - into the fully dynamic setting.
Somewhat surprisingly, our scheme produces slightly shorter signatures than the
former, thanks to a new technique for proving inequality in zero-knowledge
without relying on any inequality check. The scheme satisfies the strong
security requirements of Bootle et al.'s model (ACNS 2016), under the Short
Integer Solution (SIS) and the Learning With Errors (LWE) assumptions.
Furthermore, we demonstrate how to equip the obtained group signature scheme
with the deniability functionality in a simple way. This attractive
functionality, put forward by Ishida et al. (CANS 2016), enables the tracing
authority to provide an evidence that a given user is not the owner of a
signature in question. In the process, we design a zero-knowledge protocol for
proving that a given LWE ciphertext does not decrypt to a particular message
Learning with Errors is easy with quantum samples
Learning with Errors is one of the fundamental problems in computational
learning theory and has in the last years become the cornerstone of
post-quantum cryptography. In this work, we study the quantum sample complexity
of Learning with Errors and show that there exists an efficient quantum
learning algorithm (with polynomial sample and time complexity) for the
Learning with Errors problem where the error distribution is the one used in
cryptography. While our quantum learning algorithm does not break the LWE-based
encryption schemes proposed in the cryptography literature, it does have some
interesting implications for cryptography: first, when building an LWE-based
scheme, one needs to be careful about the access to the public-key generation
algorithm that is given to the adversary; second, our algorithm shows a
possible way for attacking LWE-based encryption by using classical samples to
approximate the quantum sample state, since then using our quantum learning
algorithm would solve LWE
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