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Projects may include studying new attacks (classical or quantum) on proposed systems, improved implementation methods for such systems, and reductions or equivalences between candidate post-quantum systems.
Successful applications will join a broad team of leading researchers in quantum computing and applied cryptography. They will also be able to take advantage of the CryptoWorks21 supplementary training program, which develops the technical and professional skills and knowledge needed to create cryptographic solutions that will be safe in a world with quantum computing technologies.
$F(X) =(A\\times X)Mod(2^r)Div(2^s)$ .\\\\* Mod is modulo operation , Div is integer division operation , A , r and s are known natural numbers while $( r > s )$ .\\\\* In this paper it is also proven that this problem is equivalent to SAT problem which is NP complete .
lightweight cryptographic design. Many lightweight block ciphers have
been proposed, targeted mostly at hardware applications. Typically software performance has not been a priority, and consequently software
performance for many of these algorithms is unexceptional. SIMON and
SPECK are lightweight block cipher families developed by the U.S. National Security Agency for high performance in constrained hardware and software environments. In this paper, we discuss software performance and demonstrate how to achieve high performance implementations of SIMON and SPECK on the AVR family of 8-bit microcontrollers. Both ciphers compare favorably to other lightweight block ciphers on this platform. Indeed, SPECK seems to have better overall performance than any existing block cipher --- lightweight or not.
Since any ideal is a module over the ring of Boolean polynomials, the change of the basis is uniquely determined by invertible matrix over the ring.
Algorithms for invertible simplifying and complicating the basis of Boolean ideal that fixes the size of basis are proposed. Algorithm of simplification optimizes the choose of pairs of polynomials during the Groebner basis computation, and eliminates variables without using resultants.
But how to optimally extract all the information contained in all possible $d$-tuples of points?
In this article, we introduce preprocessing tools that answer this question.
We first show that maximizing the higher-order CPA coefficient is equivalent to finding the maximum of the covariance.
We apply this equivalence to the problem of trace dimensionality reduction by linear combination of its samples.
Then we establish the link between this problem and the Principal Component Analysis. In a second step we present the optimal solution for the problem of maximizing the covariance.
We also theoretically and empirically compare these methods.
We finally apply them on real measurements, publicly available under the DPA Contest v4, to evaluate how the proposed techniques improve the second-order CPA (2O-CPA).