Randomizing the montgomery powering ladder

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Abstract

In this paper, we present novel randomized techniques to enhance Montgomery powering ladder. The proposed techniques increase the resistance against side-channel attacks and especially recently published correlation collision attacks in the horizontal setting. The first of these operates by randomly changing state such that the difference between registers varies, unpredictably, between two states. The second algorithm takes a random walk, albeit tightly bounded, along the possible addition chains required to compute an exponentiation. We also generalize the Montgomery powering ladder and present randomized (both left-to-right and right-to-left) m-ary exponentiation algorithms.

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APA

Le, D. P., Tan, C. H., & Tunstall, M. (2015). Randomizing the montgomery powering ladder. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9311, pp. 169–184). Springer Verlag. https://doi.org/10.1007/978-3-319-24018-3_11

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