Preventing Distinguishability between Multiplication and Squaring Operations

• URL: http://arxiv.org/abs/2604.26536v1
• Date: Wed, 29 Apr 2026 11:04:56 GMT
• Title: Preventing Distinguishability between Multiplication and Squaring Operations
• Abstract summary: Scalar multiplication kP is a critical operation in Elliptic Curve Cryptosystems.<n>Despite strategies based on atomic patterns to enhance security, the binary kP algorithms remain susceptible to simple Side-Channel Analysis (SCA)<n>This vulnerability arises independent of the multiplication method used.<n>We implemented and analysed two mitigation techniques: one involving data redirection and another focusing on bus reloading.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Scalar multiplication kP is a critical operation in Elliptic Curve Cryptosystems (ECC), often targeted by Side-Channel Analysis (SCA). Despite strategies based on atomic patterns to enhance security, the binary kP algorithms remain susceptible to simple SCA due to energy consumption variations in field multipliers during passing two different or two identical operands. This vulnerability arises independent of the multiplication method used. We implemented and analysed two mitigation techniques: one involving data redirection and another focusing on bus reloading.

Related papers

• Reinforcement Learning for Exponential Utility: Algorithms and Convergence in Discounted MDPs [2.574071344130061]
  Reinforcement learning (RL) for exponential-utility optimization in discounted Markov decision processes lacks principled value-based algorithms.<n>We derive two Q-value-style extensions and show that the associated operators are contractions in the $L_infty$ and sup-log/Thompson metrics.<n>We establish almost-sure convergence and provide finite-time convergence rates via timescale separation, and a one-timescale algorithm governed by a sublinear power-law operator.
  arXiv  Detail & Related papers  (2026-05-08T17:41:48Z)
• Automatic feature identification in least-squares policy iteration using the Koopman operator framework [0.0]
  We present a Koopman autoencoder-based least-squares policy iteration (KAE-LSPI) algorithm in reinforcement learning (RL)<n>The KAE-LSPI algorithm is based on reformulating the so-called least-squares fixed-point approximation method in terms of extended dynamic mode decomposition (EDMD)<n>We compare the KAE-LSPI algorithm with two previous works, the classical least-squares policy iteration (LSPI) and the kernel-based least-squares policy iteration (KLSPI)
  arXiv  Detail & Related papers  (2026-03-27T14:31:31Z)
• Practical Methods for Distance-Adaptive Continuous-Variable Quantum Key Distribution [2.6967769052209873]
  Continuous-variable quantum key distribution (CV-QKD) is a quantum-safe alternative to classical asymmetric cryptography.<n>FEC techniques are used to extract identical bit strings from noisy correlated data.<n>In this work, we analyze the strict limitations on operating distance that are imposed by constant-rate FEC.
  arXiv  Detail & Related papers  (2026-03-11T07:09:01Z)
• SIM-CoT: Supervised Implicit Chain-of-Thought [108.30049193668083]
  Implicit Chain-of-Thought (CoT) methods offer a token-efficient alternative to explicit CoT reasoning in Large Language Models.<n>We identify a core latent instability issue when scaling the computational budget of implicit CoT.<n>We propose SIM-CoT, a plug-and-play training module that introduces step-level supervision to stabilize and enrich the latent reasoning space.
  arXiv  Detail & Related papers  (2025-09-24T17:01:32Z)
• EconProver: Towards More Economical Test-Time Scaling for Automated Theorem Proving [64.15371139980802]
  Large Language Models (LLMs) have recently advanced the field of Automated Theorem Proving (ATP)<n>We show that different test-time scaling strategies for ATP models introduce significant computational overhead for inference.<n>We propose two complementary methods that can be integrated into a unified EconRL pipeline for amplified benefits.
  arXiv  Detail & Related papers  (2025-09-16T03:00:13Z)
• Residual-Evasive Attacks on ADMM in Distributed Optimization [2.999222219373899]
  This paper presents two attack strategies designed to evade detection in ADMM-based systems.<n>We show that our attacks remain undetected by keeping the residual largely unchanged.<n>A comparison of the two strategies, along with commonly used naive attacks, reveals trade-offs between simplicity, detectability, and effectiveness.
  arXiv  Detail & Related papers  (2025-04-22T09:12:27Z)
• Revisiting Atomic Patterns for Elliptic Curve Scalar Multiplication Revealing Inherent Vulnerability to Simple SCA [0.0]
  kP operation is the basic operation in all Elliptic Curve based cryptographic protocols.<n>In this work, we investigated the resistance of a kP algorithm implemented in hardware using Longa's atomic patterns.
  arXiv  Detail & Related papers  (2024-12-04T11:13:04Z)
• Learning Controlled Stochastic Differential Equations [61.82896036131116]
  This work proposes a novel method for estimating both drift and diffusion coefficients of continuous, multidimensional, nonlinear controlled differential equations with non-uniform diffusion. We provide strong theoretical guarantees, including finite-sample bounds for (L2), (Linfty), and risk metrics, with learning rates adaptive to coefficients' regularity. Our method is available as an open-source Python library.
  arXiv  Detail & Related papers  (2024-11-04T11:09:58Z)
• Efficient Adversarial Training in LLMs with Continuous Attacks [99.5882845458567]
  Large language models (LLMs) are vulnerable to adversarial attacks that can bypass their safety guardrails. We propose a fast adversarial training algorithm (C-AdvUL) composed of two losses. C-AdvIPO is an adversarial variant of IPO that does not require utility data for adversarially robust alignment.
  arXiv  Detail & Related papers  (2024-05-24T14:20:09Z)
• Systematic Use of Random Self-Reducibility against Physical Attacks [10.581645335323655]
  This work presents a novel, black-box software-based countermeasure against physical attacks including power side-channel and fault-injection attacks. The approach uses the concept of random self-reducibility and self-correctness to add randomness and redundancy in the execution for protection. An end-to-end implementation of this countermeasure is demonstrated for RSA-CRT signature algorithm and Kyber Key Generation public key cryptosystems.
  arXiv  Detail & Related papers  (2024-05-08T16:31:41Z)
• Temporal Difference Learning with Compressed Updates: Error-Feedback meets Reinforcement Learning [47.904127007515925]
  We study a variant of the classical temporal difference (TD) learning algorithm with a perturbed update direction. We prove that compressed TD algorithms, coupled with an error-feedback mechanism used widely in optimization, exhibit the same non-asymptotic approximation guarantees as their counterparts. Notably, these are the first finite-time results in RL that account for general compression operators and error-feedback in tandem with linear function approximation and Markovian sampling.
  arXiv  Detail & Related papers  (2023-01-03T04:09:38Z)
• Versatile Weight Attack via Flipping Limited Bits [68.45224286690932]
  We study a novel attack paradigm, which modifies model parameters in the deployment stage. Considering the effectiveness and stealthiness goals, we provide a general formulation to perform the bit-flip based weight attack. We present two cases of the general formulation with different malicious purposes, i.e., single sample attack (SSA) and triggered samples attack (TSA)
  arXiv  Detail & Related papers  (2022-07-25T03:24:58Z)
• Mitigating the Mutual Error Amplification for Semi-Supervised Object Detection [92.52505195585925]
  We propose a Cross Teaching (CT) method, aiming to mitigate the mutual error amplification by introducing a rectification mechanism of pseudo labels. In contrast to existing mutual teaching methods that directly treat predictions from other detectors as pseudo labels, we propose the Label Rectification Module (LRM)
  arXiv  Detail & Related papers  (2022-01-26T03:34:57Z)
• Targeted Attack against Deep Neural Networks via Flipping Limited Weight Bits [55.740716446995805]
  We study a novel attack paradigm, which modifies model parameters in the deployment stage for malicious purposes. Our goal is to misclassify a specific sample into a target class without any sample modification. By utilizing the latest technique in integer programming, we equivalently reformulate this BIP problem as a continuous optimization problem.
  arXiv  Detail & Related papers  (2021-02-21T03:13:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.