Cryptographic hardware and embedded systems-- CHES 2005: 7th by Josyula R. Rao, Berk Sunar

By Josyula R. Rao, Berk Sunar

This ebook constitutes the refereed complaints of the seventh foreign Workshop on Cryptographic and Embedded platforms, CHES 2005, held in Edinburgh, united kingdom in August/September 2005.

The 32 revised complete papers provided have been rigorously reviewed and chosen from 108 submissions. The papers are prepared in topical sections on part channels, mathematics for cryptanalysis, low assets, unique function undefined, assaults and countermeasures, mathematics for cryptography, relied on computing, and effective hardware.

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Extra info for Cryptographic hardware and embedded systems-- CHES 2005: 7th international workshop, Edinburgh, UK, August 29-September 1, 2005: proceedings

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Schindler, K. Lemke, and C. Paar We may assume that the functions gjt are linearly independent so that Fu;t is isomorphic to IRu . In particular, the minimum on the right-hand side of (6) always exists. Theorem 2 will turn out to be crucial for the following. In the following h∗t will always denote an element in Fu;t where (6) and (7) attain their minimum. Theorem 2. As in Theorem 1 let k ∈ {0, 1}s denote the correct subkey. e. 2 the right-hand term equals x∈{0,1}p Prob(X = x) (ht (x, k) − h (x, k)) .

0154 which although small is still 66-million times higher than a random guess. These results can also be viewed in terms of entropy loss. For a particular bit, if the classification success rate is p, then its corresponding entropy loss is given by 1 + (1 − p) log2 (1 − p) + p log2 (p). To compute the entropy loss for multiple bits we can add the individual losses (this corresponds to the worst case where classification of different bits is independent). 8-bits of entropy has been lost from the 48-bits of the DES key used in the first round (out of a maximum possible loss is 32-bits if the classification was perfect).

Rtm ) in the following. Similarly, we use the abbreviations I t (x, k), it (xj , k), ht (x, k) and h∗t (x, k), where t stands for (t1 , . . , tm ). A Stochastic Model for Differential Side Channel Cryptanalysis 37 After having determined the approximators h∗t1 , . . , h∗tm the adversary uses a second set that consists of N2 measurement curves to estimate the distribution of the m-dimensional random vector Rt = I t (X, k) − ht (X, k). We point out that in general the components Rt1 , . . , Rtm of Rt are not independent, and unlike the functions htj they hence cannot be guessed separately.

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