Grover on Present: Quantum Resource Estimation

In this work, we present cost analysis for mounting Grover\u27s key search on Present block cipher. Reversible quantum circuits for Present are designed taking into consideration several decompositions of toffoli gate. This designs are then used to produce Grover oracle for Present and their implementations cost is compared using several metrics. Resource estimation for Grover\u27s search is conducted by employing these Grover oracles. Finally, gate cost for these designs are estimated considering NIST\u27s depth restrictions

A Distinguisher on PRESENT-Like Permutations with Application to SPONGENT

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.\u27s 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

Generating graphs packed with paths: Estimation of linear approximations and differentials:Estimation of linear approximations and differentials

When designing a new symmetric-key primitive, the designer must show resistance to known attacks. Perhaps most prominent amongst these are linear and differential cryptanalysis. However, it is notoriously difficult to accurately demonstrate e.g. a block cipher’s resistance to these attacks, and thus most designers resort to deriving bounds on the linear correlations and differential probabilities of their design. On the other side of the spectrum, the cryptanalyst is interested in accurately assessing the strength of a linear or differential attack. While several tools have been developed to search for optimal linear and differential trails, e.g. MILP and SAT based methods, only few approaches specifically try to find as many trails of a single approximation or differential as possible. This can result in an overestimate of a cipher’s resistance to linear and differential attacks, as was for example the case for PRESENT. In this work, we present a new algorithm for linear and differential trail search. The algorithm represents the problem of estimating approximations and differentials as the problem of finding many long paths through a multistage graph. We demonstrate that this approach allows us to find a very large number of good trails for each approximation or differential. Moreover, we show how the algorithm can be used to efficiently estimate the key dependent correlation distribution of a linear approximation, facilitating advanced linear attacks. We apply the algorithm to 17 different ciphers, and present new and improved results on several of these

More on linear hulls of PRESENT-like ciphers and a cryptanalysis of full-round EPCBC–96

Abstract. In this paper we investigate the linear hull effect in the light-weight block cipher EPCBC. We give an efficient method of computing linear hulls with high capacity. We then apply found hulls to derive attacks on the full 32 rounds of EPCBC–96 and 20 rounds of EPCBC–48. Using the developed methods we revise the work of J.Y. Cho from 2010 and obtain an attack based on multidimensional linear approximations on 26 rounds of PRESENT–128. The results show that designers of block ciphers should take seriously the threat coming from the linear hull attacks and not just limit themselves to proving bounds based solely on linear characteristics

Towards Finding the Best Characteristics of Some Bit-oriented Block Ciphers and Automatic Enumeration of (Related-key) Differential and Linear Characteristics with Predefined Properties

In this paper, we investigate the Mixed-integer Linear Programming (MILP) modelling of the differential and linear behavior of a wide range of block ciphers. We point out that the differential behavior of an arbitrary S-box can be exactly described by a small system of linear inequalities. ~~~~~Based on this observation and MILP technique, we propose an automatic method for finding high probability (related-key) differential or linear characteristics of block ciphers. Compared with Sun {\it et al.}\u27s {\it heuristic} method presented in Asiacrypt 2014, the new method is {\it exact} for most ciphers in the sense that every feasible 0-1 solution of the MILP model generated by the new method corresponds to a valid characteristic, and therefore there is no need to repeatedly add valid cutting-off inequalities into the MILP model as is done in Sun {\it et al.}\u27s method; the new method is more powerful which allows us to get the {\it exact lower bounds} of the number of differentially or linearly active S-boxes; and the new method is more efficient which allows to obtain characteristic with higher probability or covering more rounds of a cipher (sometimes with less computational effort). ~~~~~Further, by encoding the probability information of the differentials of an S-boxes into its differential patterns, we present a novel MILP modelling technique which can be used to search for the characteristics with the maximal probability, rather than the characteristics with the smallest number of active S-boxes. With this technique, we are able to get tighter security bounds and find better characteristics. ~~~~~Moreover, by employing a type of specially constructed linear inequalities which can remove {\it exactly one} feasible 0-1 solution from the feasible region of an MILP problem, we propose a method for automatic enumeration of {\it all} (related-key) differential or linear characteristics with some predefined properties, {\it e.g.}, characteristics with given input or/and output difference/mask, or with a limited number of active S-boxes. Such a method is very useful in the automatic (related-key) differential analysis, truncated (related-key) differential analysis, linear hull analysis, and the automatic construction of (related-key) boomerang/rectangle distinguishers. ~~~~~The methods presented in this paper are very simple and straightforward, based on which we implement a Python framework for automatic cryptanalysis, and extensive experiments are performed using this framework. To demonstrate the usefulness of these methods, we apply them to SIMON, PRESENT, Serpent, LBlock, DESL, and we obtain some improved cryptanalytic results