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Fault injection attacks are a potent threat against embedded implementations of neural network models. Several attack vectors have been proposed, such as misclassification, model extraction, and trojan/backdoor planting. Most of these…

Cryptography and Security · Computer Science 2024-06-04 Patrik Velčický , Jakub Breier , Mladen Kovačević , Xiaolu Hou

We extend two of the attacks on the PLWE problem presented in (Y. Elias, K. E. Lauter, E. Ozman, and K. E. Stange, Ring-LWE Cryptography for the Number Theorist, in Directions in Number Theory, E. E. Eischen, L. Long, R. Pries, and K. E.…

Side-channel analysis attacks, especially horizontal DPA and DEMA attacks, are significant threats for cryptographic designs. In this paper we investigate to which extend different multiplication formulae and randomization of the field…

Cryptography and Security · Computer Science 2022-01-10 Ievgen Kabin , Zoya Dyka , Dan Kreiser , Peter Langendoerfer

We point out critical deficiencies in lattice-based cryptanalysis of common prime RSA presented in ``Remarks on the cryptanalysis of common prime RSA for IoT constrained low power devices'' [Information Sciences, 538 (2020) 54--68]. To…

Cryptography and Security · Computer Science 2024-01-09 Mengce Zheng

We demonstrate attacks on the boot ROMs of the Nintendo 3DS in order to exfiltrate secret information from normally protected areas of memory and gain persistent early code execution on devices which have not previously been compromised.…

Cryptography and Security · Computer Science 2018-02-05 Michael Scire , Melissa Mears , Devon Maloney , Matthew Norman , Shaun Tux , Phoebe Monroe

Linear (or differential) cryptanalysis may seem dull topics for a mathematician: they are about super simple invariants characterized by say a word on n=64 bits with very few bits at 1, the space of possible attacks is small, and basic…

Cryptography and Security · Computer Science 2019-05-14 Nicolas T. Courtois , Aidan Patrick

Correlations between input parameters play a crucial role in many scientific classification tasks, since these are often related to fundamental laws of nature. For example, in high energy physics, one of the common deep learning use-cases…

We develop a generalized framework for invariant-based cryptography by extending the use of structural identities as core cryptographic mechanisms. Starting from a previously introduced scheme where a secret is encoded via a four-point…

Cryptography and Security · Computer Science 2025-05-14 Stanislav Semenov

In a paper of P. Paillier and J. Villar a conjecture is made about the malleability of an RSA modulus. In this paper we present an explicit algorithm refuting the conjecture. Concretely we can factorize an RSA modulus n using very little…

Number Theory · Mathematics 2008-01-03 Luis Dieulefait , Jorge Jimenez Urroz

Our work focuses on modeling the security of systems from their component-level designs. Towards this goal, we develop a categorical formalism to model attacker actions. Equipping the categorical formalism with algebras produces two…

Cryptography and Security · Computer Science 2022-04-14 Georgios Bakirtzis , Fabrizio Genovese , Cody H. Fleming

Vajda and Buttyan (VB) proposed a set of five lightweight RFID authentication protocols. Defend, Fu, and Juels (DFJ) did cryptanalysis on two of them - XOR and SUBSET. To the XOR protocol, DFJ proposed repeated keys attack and nibble…

Cryptography and Security · Computer Science 2008-10-26 Xiaowen Zhang , Zhanyang Zhang , Xinzhou Wei

We introduce the new space $BV^{\alpha}(\mathbb{R}^n)$ of functions with bounded fractional variation in $\mathbb{R}^n$ of order $\alpha \in (0, 1)$ via a new distributional approach exploiting suitable notions of fractional gradient and…

Functional Analysis · Mathematics 2019-10-30 Giovanni E. Comi , Giorgio Stefani

For Arithmetization-Oriented ciphers and hash functions Gr\"obner basis attacks are generally considered as the most competitive attack vector. Unfortunately, the complexity of Gr\"obner basis algorithms is only understood for special…

Cryptography and Security · Computer Science 2024-03-05 Matthias Johann Steiner

Deep neural network models are massively deployed on a wide variety of hardware platforms. This results in the appearance of new attack vectors that significantly extend the standard attack surface, extensively studied by the adversarial…

Cryptography and Security · Computer Science 2022-10-03 Kevin Hector , Mathieu Dumont , Pierre-Alain Moellic , Jean-Max Dutertre

Ideas from Fourier analysis have been used in cryptography for the last three decades. Akavia, Goldwasser and Safra unified some of these ideas to give a complete algorithm that finds significant Fourier coefficients of functions on any…

Cryptography and Security · Computer Science 2018-12-14 Steven D. Galbraith , Joel Laity , Barak Shani

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…

Cryptography and Security · Computer Science 2026-03-23 Peipei Xie , Siwei Chen , Zejun Xiang , Shasha Zhang , Xiangyong Zeng

The security proofs of continuous-variable quantum key distribution are based on the assumptions that the eavesdropper can neither act on the local oscillator nor control Bob's beam splitter. These assumptions may be invalid in practice due…

A general study of arbitrary finite-size coherent attacks against continuous-variable quantum cryptographic schemes is presented. It is shown that, if the size of the blocks that can be coherently attacked by an eavesdropper is fixed and…

Quantum Physics · Physics 2007-05-23 Frederic Grosshans , Nicolas J. Cerf

Quantum cryptanalysis is essential for evaluating the security of cryptographic systems against the threat of quantum computing. Recently, Shi {\it et al.} introduced a dedicated quantum attack on block cipher constructions based on…

Quantum Physics · Physics 2025-11-17 Xiao-Fan Zhen , Zhen-Qiang Li , Jia-Cheng Fan , Su-Juan Qin , Fei Gao

Training modern neural networks or models typically requires averaging over a sample of high-dimensional vectors. Poisoning attacks can skew or bias the average vectors used to train the model, forcing the model to learn specific patterns…

Cryptography and Security · Computer Science 2024-12-17 Sarthak Choudhary , Aashish Kolluri , Prateek Saxena