KLEIN: A New Family of Lightweight Block Ciphers

Resource-efficient cryptographic primitives become fundamental for realizing both security and efficiency in embedded systems like RFID tags and sensor nodes. Among those primitives, lightweight block cipher plays a major role as a building block for security protocols. In this paper, we describe a new family of lightweight block ciphers named KLEIN, which is designed for resource-constrained devices such as wireless sensors and RFID tags. Compared to the related proposals, KLEIN has advantage in the software performance on legacy sensor platforms, while in the same time its hardware implementation can also be compact

Wave-Shaped Round Functions and Primitive Groups

Round functions used as building blocks for iterated block ciphers, both in the case of Substitution-Permutation Networks and Feistel Networks, are often obtained as the composition of different layers which provide confusion and diffusion, and key additions. The bijectivity of any encryption function, crucial in order to make the decryption possible, is guaranteed by the use of invertible layers or by the Feistel structure. In this work a new family of ciphers, called wave ciphers, is introduced. In wave ciphers, round functions feature wave functions, which are vectorial Boolean functions obtained as the composition of non-invertible layers, where the confusion layer enlarges the message which returns to its original size after the diffusion layer is applied. This is motivated by the fact that relaxing the requirement that all the layers are invertible allows to consider more functions which are optimal with regard to non-linearity. In particular it allows to consider injective APN S-boxes. In order to guarantee efficient decryption we propose to use wave functions in Feistel Networks. With regard to security, the immunity from some group-theoretical attacks is investigated. In particular, it is shown how to avoid that the group generated by the round functions acts imprimitively, which represent a serious flaw for the cipher

Security Analysis Techniques Using Differential Relationships For Block Ciphers

The uses of block cipher has become crucial in nowadays’ computing era as well as the information security. Information must be available only for authenticated and authorized users.However,flaws and weaknesses in the cryptosystem can breach the security of stored and transmitted information.A weak key in the key schedule is well-known issues which may affect several round keys have same bits in common.Besides,information leaked from the implementation also affects the security of block ciphers.Based on the flaws and leakage,the adversary is able to assess the differential relationships in block cipher using differential cryptanalysis technique. Firstly,the existing differential cryptanalysis techniques have been evaluated.Secondly,based on the gaps that have to be filled in the existing differential cryptanalysis techniques,new frameworks of differential cryptanalysis techniques have been proposed and designed by using Pearson correlation coefficient,Hamming-weight leakage assumption and reference point.The Pearson correlation coefficient is used to determine the repeated differential properties in the key schedules.Meanwhile, reference point and Hamming-weight leakage assumption are used to assess the security of the implementation of block ciphers against side-channel cube attack and differential fault analysis.Thirdly,all proposed frameworks have been assessed.The results show that the repeated differential properties are found for AES, PRESENT and Simeck key schedules.However,AES key schedule is definitely ideal to be adopted in the design for the future cryptographic algorithm.In addition,the newly designed frameworks for side-channel differential analysis techniques have been able to reduce the attack complexities for Simeck32/64,KATAN32 and KTANTAN32 compared to previous work.In conclusion,the proposed frameworks are effective in analyzing the security of block ciphers using differential cryptanalysis techniques

CryptoKnight:generating and modelling compiled cryptographic primitives

Cryptovirological augmentations present an immediate, incomparable threat. Over the last decade, the substantial proliferation of crypto-ransomware has had widespread consequences for consumers and organisations alike. Established preventive measures perform well, however, the problem has not ceased. Reverse engineering potentially malicious software is a cumbersome task due to platform eccentricities and obfuscated transmutation mechanisms, hence requiring smarter, more efficient detection strategies. The following manuscript presents a novel approach for the classification of cryptographic primitives in compiled binary executables using deep learning. The model blueprint, a Dynamic Convolutional Neural Network (DCNN), is fittingly configured to learn from variable-length control flow diagnostics output from a dynamic trace. To rival the size and variability of equivalent datasets, and to adequately train our model without risking adverse exposure, a methodology for the procedural generation of synthetic cryptographic binaries is defined, using core primitives from OpenSSL with multivariate obfuscation, to draw a vastly scalable distribution. The library, CryptoKnight, rendered an algorithmic pool of AES, RC4, Blowfish, MD5 and RSA to synthesise combinable variants which automatically fed into its core model. Converging at 96% accuracy, CryptoKnight was successfully able to classify the sample pool with minimal loss and correctly identified the algorithm in a real-world crypto-ransomware applicatio

07021 Abstracts Collection -- Symmetric Cryptography

From .. to .., the Dagstuhl Seminar 07021 ``Symmetric Cryptography\u27\u27 automatically was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

SANNS: Scaling Up Secure Approximate k-Nearest Neighbors Search

The $k$-Nearest Neighbor Search ($k$-NNS) is the backbone of several cloud-based services such as recommender systems, face recognition, and database search on text and images. In these services, the client sends the query to the cloud server and receives the response in which case the query and response are revealed to the service provider. Such data disclosures are unacceptable in several scenarios due to the sensitivity of data and/or privacy laws. In this paper, we introduce SANNS, a system for secure $k$-NNS that keeps client's query and the search result confidential. SANNS comprises two protocols: an optimized linear scan and a protocol based on a novel sublinear time clustering-based algorithm. We prove the security of both protocols in the standard semi-honest model. The protocols are built upon several state-of-the-art cryptographic primitives such as lattice-based additively homomorphic encryption, distributed oblivious RAM, and garbled circuits. We provide several contributions to each of these primitives which are applicable to other secure computation tasks. Both of our protocols rely on a new circuit for the approximate top-$k$ selection from $n$ numbers that is built from $O(n + k^2)$ comparators. We have implemented our proposed system and performed extensive experimental results on four datasets in two different computation environments, demonstrating more than $18-31\times$ faster response time compared to optimally implemented protocols from the prior work. Moreover, SANNS is the first work that scales to the database of 10 million entries, pushing the limit by more than two orders of magnitude.Comment: 18 pages, to appear at USENIX Security Symposium 202