A MAC Mode for Lightweight Block Ciphers

status: accepte

On the Linear Transformation in White-box Cryptography

Linear transformations are applied to the white-box cryptographic implementation for the diffusion effect to prevent key-dependent intermediate values from being analyzed. However, it has been shown that there still exists a correlation before and after the linear transformation, and thus this is not enough to protect the key against statistical analysis. So far, the Hamming weight of rows in the invertible matrix has been considered the main cause of the key leakage from the linear transformation. In this study, we present an in-depth analysis of the distribution of intermediate values and the characteristics of block invertible binary matrices. Our mathematical analysis and experimental results show that the balanced distribution of the key-dependent intermediate value is the main cause of the key leakage

The Design Space of Lightweight Cryptography

International audienceFor constrained devices, standard cryptographic algorithms can be too big, too slow or too energy-consuming. The area of lightweight cryptography studies new algorithms to overcome these problems. In this paper, we will focus on symmetric-key encryption, authentication and hashing. Instead of providing a full overview of this area of research, we will highlight three interesting topics. Firstly, we will explore the generic security of lightweight constructions. In particular, we will discuss considerations for key, block and tag sizes, and explore the topic of instantiating a pseudorandom permutation (PRP) with a non-ideal block cipher construction. This is inspired by the increasing prevalence of lightweight designs that are not secure against related-key attacks, such as PRINCE, PRIDE or Chaskey. Secondly, we explore the efficiency of cryptographic primitives. In particular, we investigate the impact on efficiency when the input size of a primitive doubles. Lastly, we provide some considerations for cryptographic design. We observe that applications do not always use cryptographic algorithms as they were intended, which negatively impacts the security and/or efficiency of the resulting implementations

White-Box Cryptography in the Gray Box - A Hardware Implementation and its Side Channels

Implementations of white-box cryptography aim to protect a secret key in a white-box environment in which an adversary has full control over the execution process and the entire environment. Its fundamental principle is the map of the cryptographic architecture, including the secret key, to a number of encoded tables that shall resist the inspection and decomposition of an attacker. In a gray-box scenario, however, the property of hiding required implementation details from the attacker could be used as a promising mitigation strategy against side-channel attacks (SCA). In this work, we present a first white-box implementation of AES on reconfigurable hardware for which we evaluate this approach assuming a gray-box attacker. We show that - unfortunately - such an implementation does not provide sufficient protection against an SCA attacker. We continue our evaluations by a thorough analysis of the source of the observed leakage, and present additional results which can be used to build stronger white-box designs

Machine Learning Assisted Differential Distinguishers For Lightweight Ciphers (Extended Version)

At CRYPTO 2019, Gohr first introduces the deep learning based cryptanalysis on round-reduced SPECK. Using a deep residual network, Gohr trains several neural network based distinguishers on 8-round SPECK-32/64. The analysis follows an `all-in-one\u27 differential cryptanalysis approach, which considers all the output differences effect under the same input difference. Usually, the all-in-one differential cryptanalysis is more effective compared to the one using only one single differential trail. However, when the cipher is non-Markov or its block size is large, it is usually very hard to fully compute. Inspired by Gohr\u27s work, we try to simulate the all-in-one differentials for non-Markov ciphers through machine learning. Our idea here is to reduce a distinguishing problem to a classification problem, so that it can be efficiently managed by machine learning. As a proof of concept, we show several distinguishers for four high profile ciphers, each of which works with trivial complexity. In particular, we show differential distinguishers for 8-round Gimli-Hash, Gimli-Cipher and Gimli-Permutation; 3-round Ascon-Permutation; 10-round Knot-256 permutation and 12-round Knot-512 permutation; and 4-round Chaskey-Permutation. Finally, we explore more on choosing an efficient machine learning model and observe that only a three layer neural network can be used. Our analysis shows the attacker is able to reduce the complexity of finding distinguishers by using machine learning techniques

Generic Round-Function-Recovery Attacks for Feistel Networks over Small Domains

Feistel Networks (FN) are now being used massively to encrypt credit card numbers through format-preserving encryption. In our work, we focus on FN with two branches, entirely unknown round functions, modular additions (or other group operations), and when the domain size of a branch (called $N$) is small. We investigate round-function-recovery attacks. The best known attack so far is an improvement of Meet-In-The-Middle (MITM) attack by Isobe and Shibutani from ASIACRYPT~2013 with optimal data complexity $q=r \frac{N}{2}$ and time complexity $N^{ \frac{r-4}{2}N + o(N)}$, where $r$ is the round number in FN. We construct an algorithm with a surprisingly better complexity when $r$ is too low, based on partial exhaustive search. When the data complexity varies from the optimal to the one of a codebook attack $q=N^2$, our time complexity can reach $N^{O \left( N^{1-\frac{1}{r-2}} \right) }$. It crosses the complexity of the improved MITM for $q\sim N\frac{\mathrm{e}^3}{r}2^{r-3}$. We also estimate the lowest secure number of rounds depending on $N$ and the security goal. We show that the format-preserving-encryption schemes FF1 and FF3 standardized by NIST and ANSI cannot offer 128-bit security (as they are supposed to) for $N\leq11$ and $N\leq17$, respectively (the NIST standard only requires $N \geq 10$), and we improve the results by Durak and Vaudenay from CRYPTO~2017

Lightweight Design Choices for LED-like Block Ciphers

Serial matrices are a preferred choice for building diffusion layers of lightweight block ciphers as one just needs to implement the last row of such a matrix. In this work we analyze a new class of serial matrices which are the lightest possible $4 \times 4$ serial matrix that can be used to build diffusion layers. With this new matrix we show that block ciphers like LED can be implemented with a reduced area in hardware designs, though it has to be cycled for more iterations. Further, we suggest the usage of an alternative S-box to the standard S-box used in LED with similar cryptographic robustness, albeit having lesser area footprint. Finally, we combine these ideas in an end-end FPGA based prototype of LED. We show that with these optimizations, there is a reduction of $16$ in area footprint of one round implementation of LED

Feistel Like Construction of Involutory Binary Matrices With High Branch Number

In this paper, we propose a generic method to construct involutory binary matrices from a three round Feistel scheme with a linear round function. We prove bounds on the maximum achievable branch number (BN) and the number of fixed points of our construction. We also define two families of efficiently implementable round functions to be used in our method. The usage of these families in the proposed method produces matrices achieving the proven bounds on branch numbers and fixed points. Moreover, we show that BN of the transpose matrix is the same with the original matrix for the function families we defined. Some of the generated matrices are \emph{Maximum Distance Binary Linear} (MDBL), i.e. matrices with the highest achievable BN. The number of fixed points of the generated matrices are close to the expected value for a random involution. Generated matrices are especially suitable for utilising in bitslice block ciphers and hash functions. They can be implemented efficiently in many platforms, from low cost CPUs to dedicated hardware