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Shasha Zhang

Publications and source records attributed to Shasha Zhang.

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From Precise to Random: A Systematic Differential Fault Analysis of the Lightweight Block Cipher Lilliput

At SAC 2013, Berger et al. first proposed the Extended Generalized Feistel Networks (EGFN) structure for the design of block ciphers with efficient diffusion. Later, based on the Type-2 EGFN, they instantiated a new lightweight block cipher named Lilliput (published in IEEE Transactions on Computers, Vol. 65, Issue 7, 2016). According to published cryptanalysis results, Lilliput is sufficiently secure against theoretical attacks such as differential, linear, boomerang, and integral attacks, which rely on the statistical properties of plaintext and ciphertext. However, there is a lack of analysis regarding its resistance to physical attacks in real-world scenarios, such as fault attacks. In this paper, we present the first systematic differential fault analysis (DFA) of Lilliput under three nibble-oriented fault models with progressively relaxed adversarial assumptions to comprehensively assess its fault resilience. In Model I (multi-round fixed-location), precise fault injections at specific rounds recover the master key with a 98% success rate using only 8 faults. Model II (single-round fixed-location) relaxes the multi-round requirement, demonstrating that 8 faults confined to a single round are still sufficient to achieve a 99% success rate by exploiting Lilliput's diffusion properties and DDT-based constraints. Model III (single-round random-location) further weakens the assumption by allowing faults to occur randomly among the eight rightmost branches of round 27. By uniquely identifying the fault location from ciphertext differences with high probability, the attack remains highly feasible, achieving over 99% success with 33 faults and exceeding 99.5% with 36 faults. Our findings reveal a significant vulnerability of Lilliput to practical fault attacks across different adversary capabilities in real-world scenarios, providing crucial insights for its secure implementation.

cs.CR

Enhancing Deep Learning-Based Rotational-XOR Attacks on Lightweight Block Ciphers Simon32/64 and Simeck32/64

At CRYPTO 2019, Gohr pioneered neural cryptanalysis by introducing differential-based neural distinguishers to attack Speck32/64, establishing a novel paradigm combining deep learning with differential cryptanalysis.Since then, constructing neural distinguishers has become a significant approach to achieving the deep learning-based cryptanalysis for block ciphers.This paper advances rotational-XOR (RX) attacks through neural networks, focusing on optimizing distinguishers and presenting key-recovery attacks for the lightweight block ciphers Simon32/64 and Simeck32/64.In particular, we first construct the fundamental data formats specially designed for training RX-neural distinguishers by refining the existing data formats for differential-neural distinguishers. Based on these data formats, we systematically identify optimal RX-differences with Hamming weights 1 and 2 that develop high-accuracy RX-neural distinguishers. Then, through innovative application of the bit sensitivity test, we achieve significant compression of data format without sacrificing the distinguisher accuracy. This optimization enables us to add more multi-ciphertext pairs into the data formats, further strengthening the performance of RX-neural distinguishers. As an application, we obtain 14- and 17-round RX-neural distinguishers for Simon32/64 and Simeck32/64, which improves the previous ones by 3 and 2 rounds, respectively.In addition, we propose two novel techniques, key bit sensitivity test and the joint wrong key response, to tackle the challenge of applying Bayesian's key-recovery strategy to the target cipher that adopts nonlinear key schedule in the related-key setting without considering of weak-key space. By this, we can straightforwardly mount a 17-round key-recovery attack on Simeck32/64 based on the improved 16-round RX-nerual distinguisher. To the best of our knowledge, the presented RX-neural......

cs.CR

The catalytic potential of high-k dielectrics for graphene formation

The growth of single and multilayer graphene nano-flakes on MgO and ZrO2 at low temperatures is shown through transmission electron microscopy. The graphene nano-flakes are ubiquitously anchored at step edges on MgO (100) surfaces. Density functional theory investigations on MgO (100) indicate C2H2 decomposition and carbon adsorption at step-edges. Hence, both the experimental and theoretical data highlight the importance of step sites for graphene growth on MgO.

cond-mat.mes-hall