AI & ML
Post-Quantum Authentication of IoT Data on Ethereum: Signatures, the Calldata Floor, and Device-Signed Merkle Roots
arXiv cs.DC0 views
arXiv:2611.08108v1 Announce Type: new Abstract: Field devices deployed today will still be producing records when quantum computers may break their signatures, so systems that anchor IoT data on blockchains need a post-quantum (PQ) authentication path now. We measure its cost on Ethereum under the current execution rules, including the EIP-7623 calldata floor and the EIP-7825 transaction cap, and under the calldata repricing scheduled for the Glamsterdam upgrade. We combine device-side measurements of ML-DSA, Falcon, SLH-DSA, LMS and XMSS, reproduced costs of open-source Solidity verifiers, a new standard-compliant LMS/HSS verifier for SHA-256 and SHA-256/192, and complete transactions on an Osaka-rule EVM, priced with today's verifiers and with proposed precompiles. Three results stand out. Signing every record costs 8.7 times as much as ECDSA even with an ML-DSA precompile, because the calldata floor rather than verification sets the price. When each device signs a Merkle root over 100 records, a standard LMS verifier available today costs about 3,350 gas per record, against 245 gas for ECDSA in the same design, and it is unaffected by the scheduled repricing, which raises floor-bound designs by up to 1.9 times. With the proposed precompiles, ML-DSA-44 costs 1,031 gas per record, and a hybrid ECDSA and ML-DSA signature adds only 2.5%. Post-quantum device authentication is therefore deployable on Ethereum without protocol changes, provided that the latency of batching and the data-availability properties of the chosen variant fit the application.
Source:https://arxiv.org/abs/2611.08108PreviousQuantum Advantage in Local Certification of Graph PropertiesNextResource-Efficient Reinforcement Learning Post-Training through Elastic Expert Parallelism
Related posts
KeHoSu: Keyed Homoglyph Substitution for Authenticated Text Provenance over Copy-Paste Channels
0arXiv cs.CR
CANMentor: A Large-Small Model Collaboration Framework for CAN Bus Intrusion Detection
0arXiv cs.CR
Beyond Instruction Override: Benchmarking Indirect Prompt Injection with Task-Tampering Attacks
0arXiv cs.CR
A Gingerbread House for LLM Agents: Source-Planned Honeypots That Lure Autonomous Attackers into an Isolated Shadow
0arXiv cs.CR
JailBound: FOL-Guided Jailbreak Evaluation for Exploring LLM Behavioral Boundaries
0arXiv cs.CR
Breaking $\mathsf{TraintoDefend}$ via Adaptive Neuron Clustering in Cryptanalytic Model Extraction
0arXiv cs.CR