International Association
for Cryptologic Research

The Peregrine signature scheme is one of the candidates in the ongoing Korean post-quantum cryptography competition. It is proposed as a high-speed variant of Falcon, which is a hash-and-sign signature scheme over NTRU lattices and one of the schemes selected by NIST for standardization. To this end, Peregrine replaces the lattice Gaussian sampler in the Falcon signing procedure with a new sampler based on the centered binomial distribution. While this modification offers significant advantages in terms of efficiency and implementation, it does not come with a provable guarantee that signatures do not leak information about the signing key. Unfortunately, lattice based signature schemes in the hash-and-sign paradigm that lack such a guarantee (such as GGH, NTRUSign or DRS) have generally proved insecure. In this paper, we show that Peregrine is no exception, by demonstrating a practical key recovery attack against it. We observe that the distribution of Peregrine signatures is a hidden transformation of some public distribution and still leaks information about the signing key. By adapting the parallelepiped-learning technique of Nguyen and Regev (Eurocrypt 2006), we show that the signing key can be recovered from a relatively small number of signatures. The learning technique alone yields an approximate version of the key, from which we can recover the exact key using a decoding technique due to Thomas Prest (PKC 2023). For the reference implementation (resp. the official specification version) of Peregrine-512, we fully recover the secret key with good probability in a few hours given around 25,000 (resp. 11 million) signature samples.

Falcon is one of the three post-quantum signature schemes selected for standardization by NIST. Due to its low bandwidth and high efficiency, Falcon is seen as an attractive option for quantum-safe embedded systems. In this work, we study Falcon’s side-channel resistance by analysing its Gaussian samplers. Our results are mainly twofold. The first result is an improved key recovery exploiting the leakage within the base sampler investigated by Guerreau et al. (CHES 2022). Instead of resorting to the fourth moment as in former parallelepiped-learning attacks, we work with the second order statistics covariance and use its spectral decomposition to recover the secret information. Our approach substantially reduces the the requirement of measurements and computation resources: 220 000 traces is sufficient to recover the secret key of Falcon-512 within half an hour with a probability of ≈ 25%. As a comparison, even with 106 traces, the former attack still needs about 1000 hours CPU time of lattice reduction for a full key recovery. In addition, our approach is robust to inaccurate leakage classification, which is another advantage over parallelepiped-learning attacks. Our second result is a practical power analysis targeting the integer Gaussian sampler of Falcon. The analysis relies on the leakage of random sign flip within the integer Gaussian sampling. This leakage was exposed in 2018 by Kim and Hong, but it is not considered in the Falcon’s implementation and unexploited for side-channel analysis until now. We identify the leakage within the reference implementation of Falcon on an ARM Cortex-M4 STM32F407IGT6 microprocessor. We also show that this single bit of leakage is in effect enough for practical key recovery: with 170 000 traces one can fully recover the key of Falcon-512 within half an hour. Furthermore, combining the sign leakage and the aforementioned leakage, one can recover the key with only 45 000 signature measurements in a short time. As a by-product, we also extend our power analysis to Mitaka that is a recent variant of Falcon. The same leakages exist within the integer Gaussian samplers of Mitaka, and they can also be used to mount key recovery attacks. Nevertheless, the key recovery in Mitaka requires much more traces than it does in Falcon, due to their different lattice Gaussian samplers.