[Resource Topic] 2022/1757: An Injectivity Analysis of CRYSTALS-Kyber and Implications on Quantum Security

Welcome to the resource topic for 2022/1757

Title:
An Injectivity Analysis of CRYSTALS-Kyber and Implications on Quantum Security

Authors: Xiaohui Ding, Muhammed F. Esgin, Amin Sakzad, Ron Steinfeld

Abstract:

The One-Way to Hiding (O2H) Lemma is a central component of proofs of chosen-ciphertext attack (CCA) security of practical
public-key encryption schemes using variants of the Fujisaki-Okamoto
(FO) transform in the Quantum Random Oracle Model (QROM). Recently, Kuchta et al. (EUROCRYPT ’20) introduced a new QROM proof technique, called Measure-Rewind-Measure (MRM), giving an improved variant of the O2H lemma, with a new security reduction that does not suffer from a square-root advantage security loss as in the earlier work of Bindel et al. (TCC ’19).However, the FO transform QROM CCA security reduction based on the improved MRM O2H lemma still requires an injectivity assumption on the underlying CPA-secure determinstic public-key encryption scheme. In particular, the tightness of the concrete security reduction relies on a sufficiently small injectivity bound, and obtaining such bounds for concrete schemes was left as an open problem by Kuchta et al. (EUROCRYPT ’20).
In this paper, we address the above problem by deriving concrete bounds on the injectivity of the deterministic CPA-secure variant of CRYSTALS-Kyber, the public-key encryption scheme selected for standardisation by the NIST Post-Quantum Cryptograpy (PQC) standardisation process. We evaluate our bounds numerically for the CRYSTALS-Kyber parameter sets, and show that the effect of injectivity on the tightness of the QROM CCA security of the Fujisaki-Okamoto transformed Kyber KEM is negligible, i.e. allows for a tight QROM CCA security reduction. Consequently, we give tightest QROM CCA security bounds to date for a simplified ‘single hashing’ variant of Kyber CCAKEM against attacks with low quantum circuit depth. Our bounds apply for all the Kyber parameter sets, based on the hardness of the Module Learning with Errors (MLWE) problem.

ePrint: https://eprint.iacr.org/2022/1757

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