Composition construction of new bent functions from known dually isomorphic bent functions

Bent functions are optimal combinatorial objects and have been studied over the last four decades. Secondary construction plays a central role in constructing bent functions since it may generate bent functions outside the primary classes of bent functions. In this study, we improve a theoretical framework of the secondary construction of bent functions in terms of the composition of Boolean functions. Based on this framework, we propose several constructions of bent functions through the composition of a balanced Boolean function and dually isomorphic (DI) bent functions defined herein. In addition, we present a construction of self-dual bent functions

Linear Hulls with Correlation Zero and Linear Cryptanalysis of Block Ciphers

Linear cryptanalysis, along with differential cryptanalysis, is an important tool to evaluate the security of block ciphers. This work introduces a novel extension of linear cryptanalysis: zero-correlation linear cryptanalysis, a technique applicable to many block cipher constructions. It is based on linear approximations with a correlation value of exactly zero. For a permutation on $n$ bits, an algorithm of complexity $2^{n-1}$ is proposed for the exact evaluation of correlation. Non-trivial zero-correlation linear approximations are demonstrated for various block cipher structures including AES, balanced Feistel networks, Skipjack, CLEFIA, and CAST256. As an example, using the zero-correlation linear cryptanalysis, a key-recovery attack is shown on 6 rounds of AES-192 and AES-256 as well as 13 rounds of CLEFIA-256

Exploiting Linear Hull in Matsui’s Algorithm 1 (extended version)

We consider linear approximations of an iterated block cipher in the presence of several strong linear approximation trails. The effect of such trails in Matsui’s Algorithm 2, also called the linear hull effect, has been previously studied by a number of authors. However, he effect on Matsui’s Algorithm 1 has not been investigated until now. In this paper, we fill this gap and examine how to exploit the linear hull in Matsui’s Algorithm 1. We develop the mathematical framework for this kind of attacks. The complexity of the attack increases with the number of strong linear trails. We show how to reduce the number of trails and thus the complexity using related keys. Further, we illustrate our theory by experimental results on a reduced round version of the block cipher PRESEN

Improved Linear Cryptanalysis of SOSEMANUK

Abstract. The SOSEMANUK stream cipher is one of the finalists of the eSTREAM project. In this paper, we improve the linear cryptanalysis of SOSEMANUK presented in Asiacrypt 2008. We apply the generalized linear masking technique to SOSEMANUK and derive many linear approximations holding with the correlations of up to 2 −25.5. We show that the data complexity of the linear attack on SOSEMANUK can be reduced by a factor of 2 10 if multiple linear approximations are used. Since SOSEMANUK claims 128-bit security, our attack would not be a real threat on the security of SOSEMANUK. Keywords: Stream Ciphers, Linear Cryptanalysis, SOSEMANUK, SOBER-128.

Linear Cryptanalysis Using Multiple Linear Approximations

In this article, the theory of multidimensional linear attacks on block ciphers is developed and the basic attack algorithms and their complexity estimates are presented. As an application the multidimensional linear distinguisher derived by Cho for the block cipher PRESENT is discussed in detail

Linear Cryptanalysis: Key Schedules and Tweakable Block Ciphers

This paper serves as a systematization of knowledge of linear cryptanalysis and provides novel insights in the areas of key schedule design and tweakable block ciphers. We examine in a step by step manner the linear hull theorem in a general and consistent setting. Based on this, we study the influence of the choice of the key scheduling on linear cryptanalysis, a – notoriously difficult – but important subject. Moreover, we investigate how tweakable block ciphers can be analyzed with respect to linear cryptanalysis, a topic that surprisingly has not been scrutinized until now

Computing Expected Differential Probability of (Truncated) Differentials and Expected Linear Potential of (Multidimensional) Linear Hulls in SPN Block Ciphers

In this paper we introduce new algorithms that, based only on the independent round keys assumption, allow to practically compute the exact expected differential probability of (truncated) differentials and the expected linear potential of (multidimensional) linear hulls. That is, we can compute the exact sum of the probability or the potential of all characteristics that follow a given activity pattern. We apply our algorithms to various recent SPN ciphers and discuss the results

Analysis of ARX Functions: Pseudo-linear Methods for Approximation, Differentials, and Evaluating Diffusion

This paper explores the approximation of addition mod $2^n$ by addition mod $2^w$, where $1 \le w \le n$, in ARX functions that use large words (e.g., 32-bit words or 64-bit words). Three main areas are explored. First, \emph{pseudo-linear approximations} aim to approximate the bits of a $w$-bit window of the state after some rounds. Second, the methods used in these approximations are also used to construct truncated differentials. Third, branch number metrics for diffusion are examined for ARX functions with large words, and variants of the differential and linear branch number characteristics based on pseudo-linear methods are introduced. These variants are called \emph{effective differential branch number} and \emph{effective linear branch number}, respectively. Applications of these approximation, differential, and diffusion evaluation techniques are demonstrated on Threefish-256 and Threefish-512

Multidimensional linear cryptanalysis

Linear cryptanalysis is an important tool for studying the security of symmetric ciphers. In 1993 Matsui proposed two algorithms, called Algorithm 1 and Algorithm 2, for recovering information about the secret key of a block cipher. The algorithms exploit a biased probabilistic relation between the input and output of the cipher. This relation is called the (one-dimensional) linear approximation of the cipher. Mathematically, the problem of key recovery is a binary hypothesis testing problem that can be solved with appropriate statistical tools. The same mathematical tools can be used for realising a distinguishing attack against a stream cipher. The distinguisher outputs whether the given sequence of keystream bits is derived from a cipher or a random source. Sometimes, it is even possible to recover a part of the initial state of the LFSR used in a key stream generator. Several authors considered using many one-dimensional linear approximations simultaneously in a key recovery attack and various solutions have been proposed. In this thesis a unified methodology for using multiple linear approximations in distinguishing and key recovery attacks is presented. This methodology, which we call multidimensional linear cryptanalysis, allows removing unnecessary and restrictive assumptions. We model the key recovery problems mathematically as hypothesis testing problems and show how to use standard statistical tools for solving them. We also show how the data complexity of linear cryptanalysis on stream ciphers and block ciphers can be reduced by using multiple approximations. We use well-known mathematical theory for comparing different statistical methods for solving the key recovery problems. We also test the theory in practice with reduced round Serpent. Based on our results, we give recommendations on how multidimensional linear cryptanalysis should be used

Comparing Large-unit and Bitwise Linear Approximations of SNOW 2.0 and SNOW 3G and Related Attacks

In this paper, we study and compare the byte-wise and bitwise linear approximations of SNOW 2.0 and SNOW 3G, and present a fast correlation attack on SNOW 3G by using our newly found bitwise linear approximations. On one side, we reconsider the relation between the large-unit linear approximation and the smallerunit/ bitwise ones derived from the large-unit one, showing that approximations on large-unit alphabets have advantages over all the smaller-unit/bitwise ones in linear attacks. But then on the other side, by comparing the byte-wise and bitwise linear approximations of SNOW 2.0 and SNOW 3G respectively, we have found many concrete examples of 8-bit linear approximations whose certain 1-dimensional/bitwise linear approximations have almost the same SEI (Squared Euclidean Imbalance) as that of the original 8-bit ones. That is, each of these byte-wise linear approximations is dominated by a single bitwise approximation, and thus the whole SEI is not essentially larger than the SEI of the dominating single bitwise approximation. Since correlation attacks can be more efficiently implemented using bitwise approximations rather than large-unit approximations, improvements over the large-unit linear approximation attacks are possible for SNOW 2.0 and SNOW 3G. For SNOW 3G, we make a careful search of the bitwise masks for the linear approximations of the FSM and obtain many mask tuples which yield high correlations. By using these bitwise linear approximations, we mount a fast correlation attack to recover the initial state of the LFSR with the time/memory/data/pre-computation complexities all upper bounded by 2174.16, improving slightly the previous best one which used an 8-bit (vectorized) linear approximation in a correlation attack with all the complexities upper bounded by 2176.56. Though not a significant improvement, our research results illustrate that we have an opportunity to achieve improvement over the large-unit attacks by using bitwise linear approximations in a linear approximation attack, and provide a new insight on the relation between large-unit and bitwise linear approximations