[Resource Topic] 2023/1352: ACE-HoT: Accelerating an extreme amount of symmetric Cipher Evaluations for High-Order avalanche Tests

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ACE-HoT: Accelerating an extreme amount of symmetric Cipher Evaluations for High-Order avalanche Tests

Authors: Emanuele Bellini, Juan Grados, Mohamed Rachidi, Nitin Satpute, Joan Daemen, Solane Elhirch


In this work, we tackle the problem of estimating the security of iterated symmetric ciphers in an efficient manner, with tests that do not require a deep analysis of the internal structure of the cipher. This is particularly useful during the design phase of these ciphers, especially for quickly testing several combinations of possible parameters defining several cipher design variants.
We consider a popular statistical test that allows us to determine the probability of flipping each cipher output bit, given a small variation in the input of the cipher. From these probabilities, one can compute three measurable metrics related to the well-known full diffusion, avalanche and strict avalanche criteria.
This highly parallelizable testing process scales linearly with the number of samples, i.e., cipher inputs, to be evaluated and the number of design variants to be tested. But, the number of design variants might grow exponentially with respect to some parameters. The high cost of CPUs, makes them a bad candidate for this kind of parallelization. As a main contribution, we propose a framework, ACE-HoT, to parallelize the testing process using multi-GPU. Our implementation does not perform any intermediate CPU-GPU data transfers.
The diffusion and avalanche criteria can be seen as an application of discrete first-order derivatives. As a secondary contribution, we generalize these criteria to their high-order version. Our generalization requires an exponentially larger number of samples, in order to compute sufficiently accurate probabilities.
As a case study, we apply ACE-HoT on most of the finalists of the NIST lightweight standardization process, with a special focus on the winner ASCON.

ePrint: https://eprint.iacr.org/2023/1352

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