Abstract:
Light nodes store a small portion of the blockchain ledger to improve scalability. In some blockchains, a malicious node can hide the invalid part of a block and make light nodes accept it. Yu et al. proposed Coded Merkle Tree (CMT) based on LDPC codes to detect DA attacks by randomly requesting/sampling portions of the block from the malicious node. If a malicious node hides a small LDPC code stopping set, light nodes are unlikely to detect a DA attack. Yu et al. used random LDPC codes to solve this problem. These codes are effective, but short code lengths, relevant for low latency systems, IoT blockchains, etc., may not be optimal for this application. This paper focuses on short code lengths and shows that a suitable co-design of specialized LDPC codes and the light node sampling strategy can improve DA attack detection. We compare adversary models that can find LDPC code stopping sets. The entropy-constrained PEG (EC-PEG) algorithm, a new LDPC code construction for a weak adversary model, concentrates stopping sets to a small group of variable nodes. EC-PEG and greedy sampling improve DA attack detection. For stronger adversary models, we co-design LP-sampling and LC-PEG algorithms for LDPC code construction. The new co-design detects DA attacks better than previous methods.
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