No abstract

In this paper, we investigate the security of Rainbow and Unbalanced Oil-and-Vinegar (UOV) signature schemes based on multivariate quadratic equations, which is one of the most promising alternatives for post-quantum signature schemes, against side-channel attacks. We describe correlation power analysis (CPA) on the schemes that yield full secret key recoveries. First, we identify a secret leakage of secret affine maps S and T during matrix-vector products in signing when Rainbow is implemented with equivalent keys rather than random affine maps for optimal implementations. In this case, the simple structure of the equivalent keys leads to the retrieval of the entire secret affine map T. Next, we extend the full secret key recovery to the general case using random affine maps via a hybrid attack: after recovering S by performing CPA, we recover T by mounting algebraic key recovery attacks. We demonstrate how this leakage on Rainbow can be practically exploited on an 8-bit AVR microcontroller using CPA. Consequently, our CPA can be applied to Rainbow-like multi-layered schemes regardless of the use of the simple-structured equivalent keys and UOV-like single layer schemes with the implementations using the equivalent keys of the simple structure. This is the first result on the security of multivariate quadratic equations-based signature schemes using only CPA. Our result can be applied to Rainbow-like multi-layered schemes and UOV-like single layer schemes submitted to NIST for Post-Quantum Cryptography Standardization.

No abstract

Correlation Power Analysis (CPA) is a very effective attack method for finding secret keys using the statistical features of power consumption signals from cryptosystems. However, the power consumption signal of the encryption device is greatly affected or distorted by noise arising from peripheral devices. When a side channel attack is carried out, this distorted signal, which is affected by noise and time inconsistency, is the major factor that reduces the attack performance. A signal processing method based on the Wavelet Transform (WT) has been proposed to enhance the attack performance. Selecting the decomposition level and the wavelet basis is very important because the CPA performance based on the WT depends on these two factors. In this paper, the CPA performance, in terms of noise reduction and the transform domain, is compared and analyzed from the viewpoint of attack time and the minimum number of signals required to find the secret key. In addition, methods for selecting the decomposition level and the wavelet basis using the features of power consumption are proposed, and validated through experiments.

No abstract

This study proposes a chosen-ciphertext sidechannel attack against a lattice-based key encapsulation mechanism (KEM), the third-round candidate of the national institute of standards and technology (NIST) standardization project. Unlike existing attacks that target operations, such as inverse NTT and message encoding/decoding, we target Barrett reduction in the decapsulation phase of CRYSTALS-KYBER to obtain a secret key. We show that a sensitive variable-dependent leakage of Barrett reduction exposes an entire secret key. The results of experiments conducted on the ARM Cortex-M4 microcontroller accomplish a success rate of 100%. We only need six chosen ciphertexts for KYBER512 and KYBER768 and eight chosen ciphertexts for KYBER1024. We also show that the m4 scheme of the pqm4 library, an implementation with the ARM Cortex-M4 specific optimization (typically in assembly), is vulnerable to the proposed attack. In this scheme, six, nine, and twelve chosen ciphertexts are required for KYBER512, KYBER768, and KYBER1024, respectively.

No abstract

Correlation Power Analysis (CPA) is a type of Side-Channel Analysis (SCA) that extracts the secret key using the correlation coefficient both side-channel information leakage by cryptography device and intermediate value of algorithms. Attack performance of the CPA is affected by noise and temporal synchronization of power consumption leaked. In the recent years, various researches about the signal processing have been presented to improve the performance of power analysis. Among these signal processing techniques, compression techniques of the signal based on Principal Component Analysis (PCA) has been presented. Selection of the principal components is an important issue in signal compression based on PCA. Because selection of the principal component will affect the performance of the analysis. In this paper, we present a method of selecting the principal component by using the correlation of the principal components and the power consumption is high and a CPA technique based on the principal component that utilizes the feature that the principal component has different. Also, we prove the performance of our method by carrying out the experiment.