In this study, the authors propose new methods using a divide-and-conquer strategy to generate n × n binary matrices (for composite n) with a high/maximum branch number and the same Hamming weight in each row and column. They introduce new types of binary matrices: namely, ( BHwC ) _{t, m} and ( BCwC ) _{q, m} types, which are a combination of Hadamard and circulant matrices, and the recursive use of circulant matrices, respectively. With the help of these hybrid structures, the search space to generate a binary matrix with a high/maximum branch number is drastically reduced. By using the proposed methods, they focus on generating 12 × 12, 16 × 16 and 32 × 32 binary matrices with a maximum or maximum achievable branch number and the lowest implementation costs (to the best of their knowledge) to be used in block ciphers. Then, they discuss the implementation properties of binary matrices generated and present experimental results for binary matrices in these sizes. Finally, they apply the proposed methods to larger sizes, i.e. 48 × 48, 64 × 64 and 80 × 80 binary matrices having some applications in secure multi-party computation and fully homomorphic encryption.

Side-channel attacks provide tools to analyse the degree of resilience of a cryptographic device against adversaries measuring leakages (e.g. power traces) on the target device executing cryptographic algorithms. In 2002, Chari et al. introduced template attacks (TA) as the strongest parametric profiled attacks in an information theoretic sense. Few years later, Schindler et al. proposed stochastic attacks (representing other parametric profiled attacks) as improved attacks (with respect to TA) when the adversary has information on the data-dependent part of the leakage. Less than ten years later, the machine learning field provided non-parametric profiled attacks especially useful in high dimensionality contexts. In this study, the authors provide new contexts in which profiled attacks based on machine learning outperform conventional parametric profiled attacks: when the set of leakages contains errors or distortions. More precisely, the authors found that (i) profiled attacks based on machine learning remain effective in a wide range of scenarios, and (ii) TA are more sensitive to distortions and errors in the profiling and attacking sets.

Scalar multiplication is the most important and expensive operation in elliptic curve cryptosystems. In this study, the authors improve the efficiency of the elliptic net algorithm to compute scalar multiplication by using the equivalence of elliptic nets. The proposed method saves four multiplications by a constant in each iteration loop. Experimental results also indicate that the proposed algorithm will be more efficient than the previously known results on this line while it is still slower than the state-of-the-art algorithm to compute scalar multiplication.

Resilient substitution boxes (S-boxes) with high non-linearity are important cryptographic primitives in the design of certain encryption algorithms. There are several trade-offs between the most important cryptographic parameters and their simultaneous optimisation is regarded as a difficult task. In this study, the authors provide a construction technique to obtain resilient S-boxes with so-called strictly almost optimal non-linearity for a larger number of output bits m than previously known. This is the first time that the non-linearity bound 2 ⁿ⁻¹ − 2 ^n/2 of resilient (n,m) S-boxes, where n and m denote the number of the input and output bits, respectively, has been exceeded for m>⌊n/4⌋. Thus, resilient S-boxes with extremely high non-linearity and a larger output space compared with other design methods have been obtained.

An announcement scheme is a system that facilitates vehicles to broadcast road-related information in vehicular ad hoc networks (VANETs) in order to improve road safety and efficiency. Here, the authors propose a new cryptographic primitive for public updating of reputation score based on the Boneh–Boyen–Shacham short group signature scheme. This allows private reputation score retrieval without a secure channel. Using this, the authors devise a privacy-aware announcement scheme using reputation systems which is reliable, auditable, and robust.

In the past decade, the researchers paid more attention to the cheating problem in visual cryptography (VC) so that many cheating prevention visual cryptography schemes (CPVCS) have been proposed. In this paper, the authors propose a novel method, which first makes use of Latin square to prevent cheating in VC. Latin squares are utilised to guide the choosing of authentication regions in different rows and columns of each divided block of the shares, which ensures that the choosing of authentication regions is both random and uniform. Without pixel expansion, the new method provides random regions authentication in each divided block of all shares. What is important is that the proposed method is applicable to both (k, n)-deterministic visual cryptography scheme ((k, n)-DVCS) and (k, n)-probabilistic visual cryptography scheme ((k, n)-PVCS). Experimental results and properties analysis are given to show the effectiveness of the proposed method.

This comment paper refers to an article published by Leu and Hsieh in IET Information Security in the year 2014. Leu and Hsieh proposed a remote user authentication protocol for distributed systems using smartcard. Their protocol affords user anonymity and no verification tables at either end, which can decrease the storage space along with the computations. Their protocol can resist security attacks and is efficient compared with few relevant protocols in terms of computational cost. However, this comment paper brings questions about the correctness of the design of Leu and Hsieh's protocol.