Read e-book online Cryptographic Hardware and Embedded Systems – CHES 2014: PDF

By Lejla Batina, Matthew Robshaw

This publication constitutes the lawsuits of the sixteenth foreign Workshop on Cryptographic and Embedded platforms, CHES 2014, held in Busan, South Korea, in September 2014. The 33 complete papers integrated during this quantity have been rigorously reviewed and chosen from 127 submissions. they're equipped in topical sections named: side-channel assaults; new assaults and buildings; countermeasures; set of rules particular SCA; ECC implementations; implementations; implementations of symmetric cryptosystems; PUFs; and RNGs and SCA matters in hardware.

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Read Online or Download Cryptographic Hardware and Embedded Systems – CHES 2014: 16th International Workshop, Busan, South Korea, September 23-26, 2014. Proceedings PDF

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K|}, the n coordinates with highest scores are the same for the two vectors. Two distinguishers d and d are then said equivalent, denoted d ≡ d if for every (xi , i )i ∈ (X × L)q , the score vectors d (xi , i )i and d (xi , i )i are rank-equivalent. In this paper, we focus on additive distinguishers which we formally define hereafter. Definition 1. A distinguisher d is additive if for every (x1 , x2 , . . , xq ) ∈ X q , there exists a family of functions {gx,k : L → R ; (x, k) ∈ X × K} such that for every ( 1 , 2 , .

We believe that using the optimized algorithm of [25] to iterate over key candidates according to probability would significantly decrease the number of tries before finding the correct key. We did not test the approach of [25] on our results. Instead, we opted to use a second power trace and intersect the candidate key-quartets (see below). Figure 5 shows how the Shanon entropy of 16 key-bytes drops as the solver uses the side channel leaks. At the beginning of the flow each key byte has 1 , giving Entropy = 128, as expected for 128 unknown bits of probability of 256 key.

8. To evaluate the classifiers performance, we define a classification error to be when the Hamming-weight with the highest probability, as predicted by the classifier, is not the correct Hamming-weight. Our decoder is far from perfect: most classifiers have an average error rate of 10-20% and some have an error rate as poor as 55%. , MixColumns). Specific classifiers’ error rates are shown in Figure 4. Note that in our framework a classifier failing to predict the exact Hammingweight as the most likely value still conveys significant information: as long as the correct Hamming-weight has higher probability than other incorrect Hammingweight classes, it helps the solver distinguish the correct values from the incorrect ones.

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