Security
Efficient and Implementation-Hardened RBLWE on Commodity Cortex-M Microcontrollers
arXiv cs.CR0 views
arXiv:2610.07820v1 Announce Type: new Abstract: Efficient post-quantum cryptography on resource-constrained Internet-of-Things (IoT) devices requires implementations that exploit the target processor architecture while resisting practical implementation attacks. This paper presents an ISA-accelerated and implementation-hardened realization of Ring Binary Learning with Errors (RBLWE) encryption on a commodity ARM Cortex-M33 microcontroller. Packing four 8-bit polynomial coefficients into the byte lanes of a 32-bit register and processing them with SIMD-style instructions, together with a packed message codec, accelerates encryption and decryption, while a buffered hardware-TRNG entropy source drawn from the on-die Secure Element drives the key-generation gain. Together these give same-core cold-start speedups of $4.06\times$, $3.35\times$, and $3.01\times$ for key generation, encryption, and decryption over a scalar baseline, and $3.52\times$/$3.18\times$ lower encryption/decryption cycle counts than a reference Cortex-M0 implementation. On top of this accelerated core, we add four staged countermeasures: constant-time execution, fault hardening against zeroing, random-corruption, and instruction-skip faults, a Fujisaki-Okamoto (FO)-style CCA2 transform, and first-order shared (masked) CPA decryption, reporting each layer's cost individually. Binary-level inspection confirms these countermeasures survive compilation and identifies a compiler-induced masking flaw resolved with a hand-written assembly replacement. Dudect-style timing tests, debugger-assisted fault-injection campaigns, and component-level TVLA then provide implementation-level evidence for the staged protections. The results demonstrate a practical acceleration-security tradeoff for RBLWE on off-the-shelf microcontrollers and reusable architecture-aware techniques for lightweight post-quantum implementations.
Source:https://arxiv.org/abs/2610.07820PreviousSCSM: A Traffic-Native Foundation Model for Transferable Website FingerprintingNextLifecycle-Based Design and Evaluation of Real-Time Backup Triggers for Ransomware Damage Mitigation
Related posts
CVE-2026-106074: a build-time randomness leak in Node.js snapshots
0dev.to Node
LanG 2.0: Energy-Efficient Agentic AI for Semi-Automated Security Operations at Scale
0arXiv cs.CR
Characterizing Recoverable Expert-Utilization Signatures in Fine-Tuned Mixture-of-Experts LLMs
0arXiv cs.CR
Mutipath TCP: Use and Detection, (Mon, Nov 2nd)
0SANS ISC
AI Vulnerability Finding
0Schneier on Security
[$] Managing the kernel CVE flood
0LWN.net