Cryptology ePrint Archive: Report 2016/236

A Distinguisher on PRESENT-Like Permutations with Application to SPONGENT

Guoyan Zhang and Meicheng Liu

Abstract: At Crypto 2015, Blondeau et al. showed a known-key analysis on the full PRESENT lightweight block cipher. Based on some of the best differential distinguishers, they introduced a meet in the middle (MitM) layer to pre-add the differential distinguisher, which extends the number of attacked rounds on PRESENT from 26 rounds to full rounds without reducing differential probability.

In this paper, we generalize their method and present a distinguisher on a kind of permutations called PRESENT-like permutations. This generic distinguisher is divided into two phases. The first phase is a truncated differential distinguisher with strong bias, which describes the unbalancedness of the output collision on some fixed bits, given the fixed input in some bits, and we take advantage of the strong relation between truncated differential probability and capacity of multidimensional linear approximation to derive the best differential distinguishers. The second phase is the meet-in-the-middle layer, which is pre-added to the truncated differential to propagate the differential properties as far as possible. Different with Blondeau et al.'s work, we extend the MitM layers on a 64-bit internal state to states with any size, and we also give a concrete bound to estimate the attacked rounds of the MitM layer.

As an illustration, we apply our technique to all versions of SPONGENT permutations. In the truncated differential phase, as a result we reach one, two or three rounds more than the results shown by the designers. In the meet-in-the-middle phase, we get up to 11 rounds to pre-add to the differential distinguishers. Totally, we improve the previous distinguishers on all versions of SPONGENT permutations by up to 13 rounds.

Category / Keywords: secret-key cryptography / symmetric ciphers, PRESENT, SPONGENT, truncated differential, meet-in-the-middle, multidimensional linear approximation.

Date: received 3 Mar 2016, last revised 3 Mar 2016

Contact author: guoyanzhang at sdu edu cn; meicheng liu@gmail com

Available format(s): PDF | BibTeX Citation

Version: 20160304:024320 (All versions of this report)

Short URL: ia.cr/2016/236

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