MergeMAC:A MAC for Authentication with Strict Time Constraints and Limited Bandwidth

This paper presents MergeMAC, a MAC that is particularly suitable for environments with strict time requirements and extremely limited bandwidth. MergeMAC computes the MAC by splitting the message into two parts. We use a pseudorandom function (PRF) to map messages to random bit strings and then merge them with a very efficient keyless function. The advantage of this approach is that the outputs of the PRF can be cached for frequently needed message parts. We demonstrate the merits of MergeMAC for authenticating messages on the CAN bus where bandwidth is extremely limited and caching can be used to recover parts of the message counter instead of transmitting it. We recommend an instantiation of the merging function MERGE and analyze the security of our construction. Requirements for a merging function are formally defined and the resulting EUF-CMA security of MergeMAC is proven

Rotational Cryptanalysis on MAC Algorithm Chaskey

In this paper we analyse the algorithm Chaskey - a lightweight MAC algorithm for 32-bit micro controllers - with respect to rotational cryptanalysis. We perform a related-key attack over Chaskey and find a distinguisher by using rotational probabilities. Having a message $m$ we can forge and present a valid tag for some message under a related key with probability $2^{-57}$ for 8 rounds and $2^{-86}$ for all 12 rounds of the permutation for keys in a defined weak-key class. This attack can be extended to full key recovery with complexity $2^{120}$ for the full number of rounds. To our knowledge this is the first published attack targeting all 12 rounds of the algorithm. Additionally, we generalize the Markov theory with respect to a relation between two plaintexts and not their difference and apply it for rotational pairs

Rotational-Linear Attack: A New Framework of Cryptanalysis on ARX ciphers with Applications to Chaskey

In this paper, we formulate a new framework of cryptanalysis called rotational-linear attack on ARX ciphers. We firstly build an efficient distinguisher for the cipher $E$ consisted of the rotational attack and the linear attack together with some intermediate variables. Then a key recovery technique is introduced with which we can recover some bits of the last whitening key in the related-key scenario. To decrease data complexity of our attack, we also apply a new method, called bit flipping, in the rotational cryptanalysis for the first time and the effective partitioning technique to the key-recovery part. Applying the new framework of attack to the MAC algorithm Chaskey, we build a full-round distinguisher over it. Besides, we have recovered $21$ bits of information of the key in the related-key scenario, for keys belonging to a large weak-key class based on 6-round distinguisher. The data complexity is $2^{38.8}$ and the time complexity is $2^{46.8}$. Before our work, the rotational distinguisher can only be used to reveal key information by checking weak-key conditions. This is the first time it is applied in a last-rounds key-recovery attack. We build a 17-round rotational-linear distinguisher for ChaCha permutation as an improvement compared to single rotational cryptanalysis over it

A Survey of ARX-based Symmetric-key Primitives

Addition Rotation XOR is suitable for fast implementation symmetric –key primitives, such as stream and block ciphers. This paper presents a review of several block and stream ciphers based on ARX construction followed by the discussion on the security analysis of symmetric key primitives where the best attack for every cipher was carried out. We benchmark the implementation on software and hardware according to the evaluation metrics. Therefore, this paper aims at providing a reference for a better selection of ARX design strategy

Rotational Cryptanalysis in the Presence of Constants

Rotational cryptanalysis is a statistical method for attacking ARX constructions. It was previously shown that ARX-C, i.e., ARX with the injection of constants can be used to implement any function. In this paper we investigate how rotational cryptanalysis is affected when constants are injected into the state. We introduce the notion of an RX-difference, generalizing the idea of a rotational difference. We show how RX-differences behave around modular addition, and give a formula to calculate their transition probability. We experimentally verify the formula using Speck32/64, and present a 7-round distinguisher based on RX-differences. We then discuss two types of constants: round constants, and constants which are the result of using a fixed key, and provide recommendations to designers for optimal choice of parameters

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

A Novel System for Confidential Medical Data Storage Using Chaskey Encryption and Blockchain Technology

يعد التخزين الآمن للمعلومات الطبية السرية أمرًا بالغ الأهمية لمنظمات الرعاية الصحية التي تسعى إلى حماية خصوصية المريض والامتثال للمتطلبات التنظيمية. في هذا البحث، نقدم نظامًا جديدًا للتخزين الآمن للبيانات الطبية باستخدام تقنية تشفير Chaskey و blockchain. يستخدم النظام تشفير Chaskey لضمان سرية وسلامة البيانات الطبية، وتكنولوجيا blockchain لتوفير حلول تخزين البيانات الطبية بحيث يكون قابل للتطوير ويتميز باللامركزية. يستخدم النظام أيضًا تقنيات Bflow للتجزئة ومنها التجزئة الرأسية لتعزيز قابلية التوسع وإدارة البيانات المخزنة. بالإضافة إلى ذلك، يستخدم النظام العقود الذكية لفرض سياسات التحكم في الوصول والتدابير الأمنية الأخرى. سنقدم وصف للنظام المقترح بالتفصيل ونقدم تحليلاً لخصائصه الأمنية والأداء. تظهر نتائجنا أن النظام يوفر حلاً آمنًا للغاية وقابل للتطوير لتخزين البيانات الطبية السرية، مع تطبيقات محتملة في مجموعة واسعة من إعدادات الرعاية الصحية.Secure storage of confidential medical information is critical to healthcare organizations seeking to protect patient's privacy and comply with regulatory requirements. This paper presents a new scheme for secure storage of medical data using Chaskey cryptography and blockchain technology. The system uses Chaskey encryption to ensure integrity and confidentiality of medical data, blockchain technology to provide a scalable and decentralized storage solution. The system also uses Bflow segmentation and vertical segmentation technologies to enhance scalability and manage the stored data. In addition, the system uses smart contracts to enforce access control policies and other security measures. The description of the system detailing and provide an analysis of its security and performance characteristics. The resulting images were tested against a number of important metrics such as Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), bit error rate (BER), Signal-to-Noise Ratio (SNR), Normalization Correlation (NC) and Structural Similarity Index (SSIM). Our results showing that the system provides a highly secure and scalable solution for storing confidential medical data, with potential applications in a wide range of healthcare settings

Rotational Differential-Linear Distinguishers of ARX Ciphers with Arbitrary Output Linear Masks

The rotational differential-linear attacks, proposed at EUROCRYPT 2021, is a generalization of differential-linear attacks by replacing the differential part of the attacks with rotational differentials. At EUROCRYPT 2021, Liu et al. presented a method based on Morawiecki et al.’s technique (FSE 2013) for evaluating the rotational differential-linear correlations for the special cases where the output linear masks are unit vectors. With this method, some powerful (rotational) differential-linear distinguishers with output linear masks being unit vectors against Friet, Xoodoo, and Alzette were discovered. However, how to compute the rotational differential-linear correlations for arbitrary output masks was left open. In this work, we partially solve this open problem by presenting an efficient algorithm for computing the (rotational) differential-linear correlation of modulo additions for arbitrary output linear masks, based on which a technique for evaluating the (rotational) differential-linear correlation of ARX ciphers is derived. We apply the technique to Alzette, Siphash, Chacha, and Speck. As a result, significantly improved (rotational) differential-linear distinguishers including deterministic ones are identified. All results of this work are practical and experimentally verified to confirm the validity of our methods. In addition, we try to explain the experimental distinguishers employed in FSE 2008, FSE 2016, and CRYPTO 2020 against Chacha. The predicted correlations are close to the experimental ones

An Easy-to-Use Tool for Rotational-XOR Cryptanalysis of ARX Block Ciphers

An increasing number of lightweight cryptographic primitives have been published recently. Some of these proposals are ARX primitives, which have shown a great performance in software. Rotational-XOR cryptanalysis is a statistical technique to attack ARX primitives. In this paper, a computer tool to speed up and make easier the security evaluation of ARX block ciphers against rotational-XOR cryptanalysis is shown. Our tool takes a Python implementation of an ARX block cipher and automatically finds an optimal rotational-XOR characteristic. Compared to most of the automated tools, which only support a small set of primitives, our tool supports any ARX block cipher and it is executed with a simple shell command

Characteristic Automated Search of Cryptographic Algorithms for Distinguishing Attacks (CASCADA)

Automated search methods based on Satisfiability Modulo Theories (SMT) problems are being widely used to evaluate the security of block ciphers against distinguishing attacks. While these methods provide a systematic and generic methodology, most of their software implementations are limited to a small set of ciphers and attacks, and extending these implementations requires significant effort and expertise. In this work we present CASCADA, an open-source Python library to evaluate the security of cryptographic primitives, specially block ciphers, against distinguishing attacks with bit-vector SMT solvers. The tool CASCADA implements the bit-vector property framework herein proposed and several SMT-based automated search methods to evaluate the security of ciphers against differential, related-key differential, rotational-XOR, impossible-differential, impossible-rotational-XOR, related-key impossible-differential, linear and zero-correlation cryptanalysis. The library CASCADA is the result of a huge engineering effort, and it provides many functionalities, a modular design, an extensive documentation and a complete suite of tests