Protecting Blockchain Systems from Data Availability Attacks: Short Code-Length LDPC Code Design for Coded Merkle Tree

PROJECT TITLE :

Overcoming Data Availability Attacks in Blockchain Systems: Short Code-Length LDPC Code Design for Coded Merkle Tree

ABSTRACT:

Light nodes are a type of node that can be found in blockchains. These nodes only store a small portion of the Blockchain ledger, but they help make the system more scalable. In some blockchains, light nodes are susceptible to an attack known as a data availability (DA) attack. This type of attack occurs when a malicious node tricks the light nodes into accepting an invalid block by concealing the invalid portion of the block from the other nodes in the system. Coded Merkle Tree (CMT), which is based on LDPC codes and was recently proposed by Yu et al., is a method that enables light nodes to detect a DA attack by randomly requesting or sampling portions of the block from a malicious node. CMT is a technique that was developed by Yu et al. However, in the event that a malicious node conceals a small stopping set of the LDPC code, there is a significant chance that light nodes will be unable to detect a DA attack. Yu et al. utilized random LDPC codes in order to find a solution to this issue. These codes are able to achieve a large minimum stopping set size with a high probability. Even though they work, these codes are not necessarily the best choice for this application. This is especially true at shorter code lengths, which are important for low-latency systems and blockchains for Internet of Things devices, among other things. In this paper, we focus on short code lengths and demonstrate that a suitable co-design of specialized LDPC codes and the light node sampling strategy can improve the probability of detecting DA attacks. We do this by demonstrating that a suitable co-design of short code lengths can improve the probability of detecting DA attacks. We look at a variety of adversary models and evaluate them according to the computational power they bring to the task of discovering stopping sets in LDPC codes. We develop a new LDPC code construction for a weak adversary model that we call the entropy-constrained PEG (EC-PEG) algorithm. This algorithm concentrates stopping sets on a small group of variable nodes and is called the EC-PEG code construction. We show that the probability of detecting DA attacks can be improved by combining the EC-PEG algorithm with a greedy sampling strategy. A co-design of a sampling strategy known as linear-programming-sampling (LP-sampling) and an LDPC code construction known as the linear-programming-constrained PEG (LC-PEG) algorithm is what we offer as a solution for adversary models that are more powerful. In comparison to the methods described in earlier research literature, the newly developed co-design demonstrates a significantly increased likelihood of DA attack detection.

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