Vertical and horizontal correlation attacks on RNS-based exponentiations - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Journal Articles Journal of Cryptographic Engineering Year : 2015

Vertical and horizontal correlation attacks on RNS-based exponentiations

Laurent Imbert
Philippe Maurine
Lionel Torres


Side-channel attacks are a serious threat for physical implementations of public-key cryptosystems and notably for the RSA. Side-channel leakages can be explored from unprotected cryptodevices and several power or electromagnetic traces are collected in order to construct (vertical) differential side-channel attacks. On exponentiations, the so-called horizontal correlation attacks originally proposed by Walter in 2001 and improved by Clavier et al in 2010 demonstrated to be efficient even in the presence of strong countermeasures like the exponent and message blinding. In particular, a single trace is sufficient to recover the secret if the modular exponentiation features long-integer multiplications. In this paper, we consider the application of vertical and horizontal correlation attacks on RNS-based approaches. The Montgomery multiplication, which is widely adopted in the finite ring of an exponentiation, has different construction details in the RNS domain. Experiments are conducted on hardware (parallel) and software (sequential) and leakage models for known and masked inputs are constructed for the regular and SPA-protected Montgomery ladder algorithm.
Fichier principal
Vignette du fichier
paper_JCE_v4_revised.pdf (6.33 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

lirmm-01269799 , version 1 (05-02-2016)



Guilherme Perin, Laurent Imbert, Philippe Maurine, Lionel Torres. Vertical and horizontal correlation attacks on RNS-based exponentiations. Journal of Cryptographic Engineering, 2015, 5 (3), pp.171-185. ⟨10.1007/s13389-015-0095-0⟩. ⟨lirmm-01269799⟩
228 View
597 Download



Gmail Facebook X LinkedIn More