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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao https://doi.org/10.1...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
https://doi.org/10.1007/978-98...
Part of book or chapter of book . 2020 . Peer-reviewed
License: Springer TDM
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Near and Far Collision Attack on Masked AES

Authors: An Wang; Qingping Tang; Xiaoya Yang; Yaoling Ding; Jiawei Zhang; Yongchuan Niu;

Near and Far Collision Attack on Masked AES

Abstract

Collision attack is an effective method in the field of side-channel analysis to crack cryptographic algorithms, and masking can be used as a countermeasure. Most collision attacks only utilize the traces that will collide. In this paper, we propose a collision attack method that exploits not only traces tending to collide, but also non-colliding traces. It can bring higher efficiency and reduce the number of needed traces significantly. In addition, our method is a random-plaintext collision attack method instead of a chosen-plaintext attack. The experimental results show that our proposed approach is better than the existing collision-correlation attack proposed by Clavier et al. at CHES 2011 [11]. To achieve a high key recovery success rate at 80%, we use at least 60% less traces than collision-correlation attack.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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