2020/08/19 by Dongfang Zhao, Zhao, Dongfang
Computer Science · #Blockchain Technology Applications and Security #Cognitive Computing and Networks #Cryptography and Security (cs.CR) #Distributed #FOS: Computer and information sciences #Graph Theory and Algorithms #Parallel #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2008.08208
openalex publication_date 2020/08/19 · openalex created_date 2020/08/24 · openalex updated_date 2026/07/28
The state-of-the-art techniques for processing cross-blockchain transactions take a simple centralized approach: when the assets on blockchain X, say X-coins, are exchanged with the assets on blockchain Y---the Y-coins, those X-coins need to be exchanged to a "middle" medium (such as Bitcoin) that is then exchanged to Y-coins. If there are more than two parties involved in a single global transaction, the global transaction is split into multiple local two-party transactions, each of which follows the above central-exchange protocol. Unfortunately, the atomicity of the global transaction is violated with the central-exchange approach: those local two-party transactions, once committed, cannot be rolled back if the global transaction decides to abort. In a more general sense, the graph-based model of (two-party) transactions can hardly be extended to an arbitrary number of parties in a cross-blockchain transaction. %from why to how In this paper, we introduce a higher-level abstraction of cross-blockchain transactions. We adopt the abstract simplicial complex, an extensively-studied mathematical object in algebraic topology, to represent an arbitrary number of parties involved in the blockchain transactions. Essentially, each party in the global transaction is modeled as a vertex and the global transaction among n+1 (n ∈ ℤ, n > 0) parties compose a n-dimensional simplex. While this higher-level abstraction seems plausibly trivial, we will show how this simple extension leads to a new line of modeling methods and protocols for better processing cross-blockchain transactions.