~ similar to 2604.21261v1· 20 results
Jianan Mu, Ge Yu, Tenghui Hua, Liang Kong +5 more
This paper presents an efficient fault-tolerance scheme for CPU-based fully homomorphic encryption (FHE) that reduces protection overhead and achieves 100 percent detection rate under random single-bi…
Ryan Babbush, Adam Zalcman, Craig Gidney, Michael Broughton +5 more
The paper estimates the quantum resources required to break 256-bit ECC cryptography and warns that fast-clock quantum computers could enable on-spend attacks on modern cryptocurrencies, necessitating…
The paper introduces the base-m length codec, a canonical and robust encoding scheme that maps byte strings to lists of residues modulo m, essential for finite-ring cryptosystems.
The paper applies Stringology-Based Cryptanalysis (SBC) using KMP and Boyer-Moore algorithms to analyze EChaCha20, confirming that the cipher maintains strong pseudorandomness and exhibits rapid diffu…
This paper addresses the persistent side-channel vulnerability of scalar multiplication in ECC, proposing and analyzing two mitigation techniques—data redirection and bus reloading—to counter energy c…
The paper proposes a novel space switching method to efficiently unify arithmetic and comparison operations within Fully Homomorphic Encryption (FHE) schemes, achieving significant performance improve…
The paper introduces a semantics-first verification framework for an implemented Shor oracle for ECDLP in Qrisp, demonstrating that even seemingly correct implementations can fail due to subtle contro…
The paper introduces 'quantum-safe,' a Python library that addresses the remaining 'production gap' in post-quantum cryptography (PQC) by providing robust, easy-to-use hybrid implementations and compr…
This paper provides a comparative analysis and benchmarking of Secure Multi-Party Computation (SMPC) and Fully Homomorphic Encryption (FHE) for machine learning, finding that the optimal choice depend…
Yusheng Zheng, Zhengjie Ji, Weichen Tao, Hao Sun +3 more
The paper introduces Kops, an extension interface for eBPF that allows userspace compilers and kernel modules to introduce new operations without modifying the kernel core, improving performance and r…
Fengxia Liu, Zixian Gong, Kun Tian, Yi Zhang +2 more
The paper introduces a unified framework for Quantum Fully Homomorphic Encryption (QFHE) that achieves exponential efficiency improvements by integrating a novel modular arithmetic program (MAP) tailo…
The paper reveals that predictable nonce reuse by Polygon MEV searchers creates a critical vulnerability in ECDSA signatures, allowing passive attackers to recover private keys using linear algebra.
The paper introduces public-decay Homomorphic State Space Models (HSSMs) that enable efficient, high-accuracy sequence inference directly on encrypted data, significantly outperforming existing encryp…
The paper presents a lattice-based Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme that supports $\mathsf{NC}^1$ access policies while maintaining constant-size ciphertexts.
The paper analyzes the differential properties of the SIMON32 cipher, identifying high-probability differentials to improve the efficiency and depth of cryptanalysis beyond current state-of-the-art me…
This paper fixes two subtle bugs in Go's extended GCD implementation, which is critical for RSA key generation, and formally proves the correctness and termination of the corrected code.
Jianan Mu, Ge Yu, Zhaoxuan Kan, Song Bian +5 more
This paper evaluates the vulnerability of Fully Homomorphic Encryption (FHE) computation to silent data corruption (SDC) using large-scale fault-injection experiments and theoretical analysis.
Yiping Ma, Yu-Lin Tsai, Mayank Rathee, Deevashwer Rathee +3 more
This paper introduces ShannonProver, an agentic framework that automates cryptographic proofs in EasyCrypt, allowing for scalable proof verification.
This paper demonstrates that standard binary kP algorithms, even when protected using Chevallier-Mames atomic blocks, remain vulnerable to single-trace Side-Channel Analysis (SCA) attacks.
This paper provides a comprehensive, system-level comparison of MPC and FHE for Privacy-Preserving Machine Learning (PPML) across various models and environments, moving beyond single-metric latency a…