arXiv subjects

Ravi Anand

Publications and source records attributed to Ravi Anand.

4 recordsLinked to original sources

arXiv2026

Masked Differential-linear Distinguishers and Quantum Approaches

We introduce masked auto-correlation, a new primitive for the cryptanalysis of symmetric-key primitives, together with a quantum attack pipeline built on it. For a permutation $f$, output masks $\alpha,\beta$, and an input difference $w$, masked auto-correlation (MAC) measures the correlation between the masked outputs $\alpha\cdot f(x)$ and $\beta\cdot f(x\oplus w)$. The associated masked differential-linear (MDL) approximations strictly generalize several classical techniques; ordinary linear cryptanalysis, differential-linear cryptanalysis, and the differential-linear connectivity table all arise as special cases. Our central object of study is the problem of finding mask pairs with large masked cross-correlation -- those that yield powerful distinguishers -- which we call MAC Fishing. We give a constant-query quantum algorithm that samples such pairs according to their squared correlation, and we prove an exponential classical lower bound of $\Omega(N/\log N)$ queries, by adapting the hardness of Fourier Fishing. To our knowledge this is the first result pairing a quantum upper bound with a classical lower bound for the core task of identifying high-correlation approximations, making quantum algorithms an absolute necessity. Building on this, we analyse the distribution of masked auto-correlation for random permutations, and then construct capacity-based distinguishers and key-recovery attacks, both classically and with a quadratic quantum speed-up using amplitude estimation. We validate our claims with experiments on reduced-round mini-AES.

quant-ph↗

arXiv2024

A Comprehensive Review of Adversarial Attacks on Machine Learning

This research provides a comprehensive overview of adversarial attacks on AI and ML models, exploring various attack types, techniques, and their potential harms. We also delve into the business implications, mitigation strategies, and future research directions. To gain practical insights, we employ the Adversarial Robustness Toolbox (ART) [1] library to simulate these attacks on real-world use cases, such as self-driving cars. Our goal is to inform practitioners and researchers about the challenges and opportunities in defending AI systems against adversarial threats. By providing a comprehensive comparison of different attack methods, we aim to contribute to the development of more robust and secure AI systems.

cs.CR↗

arXiv2021

Linear Cryptanalysis through the Lens of Clauser-Horne-Shimony-Holt Game

Application of CHSH game in Linear Cryptanalysis is presented. Till date, the known usage of CHSH game in Quantum Cryptology is to verify the device independence of the protocols. We observed that the classical symmetric ciphers having the bias equal to 0:25 can be improved to 0:35 exploiting the game which indicates clear improvement over existing Linear and Differential cryptanalysis. In the present initiative, we showed the application of the game in linear cryptanalysis on a lightweight cipher named SIMON. However, the approach can be extended to Differential cryptanalysis too. This observation opens a new direction of research in quantum cryptography.

quant-ph↗

arXiv2020

Grover on SIMON

For any symmetric key cryptosystem with $n$-bit secret key, the key can be recovered in $O(2^{n/2})$ exploiting Grover search algorithm, resulting in the effective key length to be half. In this direction, subsequent work has been done on AES and some other block ciphers. On the other hand, lightweight ciphers like SIMON was left unexplored. In this backdrop, we present Grover's search algorithm on all the variants of SIMON and enumerate the quantum resources to implement such attack in terms of NOT, CNOT and Toffoli gates. We also provide the T-depth of the circuits and the number of qubits required for the attack. We show that the number of qubits required for implementing Grover on SIMON $2n/mn$ is $O(2nr+mn)$, where $r$ is the number of chosen plaintext-cipher text pairs. We run a reduced version of SIMON in IBMQ quantum simulator and the 14-qubits processor as well. We found that where simulation supports theory, the actual implementation is far from the reality due to the infidelity of the gates and short decoherence time of the qubits. The complete codes for all version of SIMON have also been presented.

quant-ph↗