Titlebook: Symposia on Theoretical Physics; 2 Lectures presented Alladi Ramakrishnan (Director of the Institute) Book 1966 Plenum Press 1966 Potential

IRATE

plication at classical cryptography security levels (NIST curves). Our implementation uses 35 % less LUTs compared to the state of the art generic implementation of ECC using RNS for the same performance [.]. We also slightly improve the computation time (latency) and our implementation shows best r

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Palpate

L. O’Raifeartaighced at CHES 2012. We first detail is performances. Then we analyze it against standard differential power analysis and discuss the different parameters influencing its security bounds. Finally, we put forward that its implementation in 8-bit microcontrollers can provide a better security vs. perform

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Lacerate

R. Hagedornssembly generation. For this purpose, our first contribution is a simple reduction from security proofs obtained in a (usual but not always realistic) model where leakages depend on the intermediate variables manipulated by the target device, to security proofs in a (more realistic) model where the

红肿

M. Jacobplication at classical cryptography security levels (NIST curves). Our implementation uses 35 % less LUTs compared to the state of the art generic implementation of ECC using RNS for the same performance [.]. We also slightly improve the computation time (latency) and our implementation shows best r

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K. Venkatesanalance) best choice is to utilise an acyclic graph in an independent graph combination setting, which gives us provable convergence to the correct key distribution. We provide evidence using both extensive simulations and a final confirmatory analysis on real trace data.

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B. M. Udgaonkaro improve the algebraic complexity of the scheme) with standard additive Boolean masking (to allow efficient higher-order implementations). We show that such a masking is well suited for application to bitslice ciphers. We also conduct a comprehensive security analysis of the proposed scheme. For th

HERTZ

A. N. Mitraformative feature extraction in presence of masking countermeasure. This method, called Kernel Discriminant Analysis, consists in completing the Linear Discriminant Analysis with a so-called kernel trick, in order to efficiently perform it over the set of all possible .-tuples of points without grow