Decoding Distributed Ledger Transactions via ABI Fingerprinting
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Solution Overview
Problem
Decoding distributed ledger transaction records is challenging due to cryptographically protected nature and the lack of clear parameter names in smart contract bytecode, especially when multiple smart contracts have overlapping function signatures.
Innovation Solution
Implementing digital fingerprinting of smart contracts based on their Application Binary Interface (ABI) definitions to uniquely identify and decode transactions by comparing computed digital fingerprints with stored ABI definitions, facilitating the extraction of function names, parameters, and values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If digital fingerprinting is implemented to uniquely identify smart contracts, then transaction decoding accuracy is improved, but system complexity increases
Solution Approach 1:
The patent introduces digital fingerprints as an intermediary element that mediates between the cryptographic transaction data and the ABI definitions. The fingerprint acts as a unique identifier that bridges the gap between opaque bytecode and meaningful function signatures, enabling accurate decoding without directly comparing entire ABI definitions during transaction processing.
Solution Approach 2:
The system performs preliminary computation of digital fingerprints for all smart contracts and stores them in advance. This pre-computation allows the decoding system to quickly match transaction data against known contracts using simple fingerprint comparison rather than complex ABI analysis during live transaction processing, thereby improving accuracy while managing system complexity through advance preparation.
2Reliability
If cryptographic protection is maintained for distributed ledger transactions, then security is improved, but data extractability deteriorates
Solution Approach 1:
The patent extracts the essential identifying feature (digital fingerprint) from the cryptographic transaction data without breaking the cryptographic protection. By taking out only the necessary fingerprint information for matching purposes while leaving the rest of the cryptographic structure intact, the system maintains security while enabling data extractability through the fingerprint-ABI matching mechanism.
Data Source
AI summary
Systems and methods for decoding distributed ledger transactions by data intake and query systems. An example method includes: receiving a transaction of a distributed ledger, wherein the transaction includes transaction data and an identifier of an account of the distributed ledger; receiving a bytecode module, wherein the bytecode module is associated with the account of the distributed ledger; computing a bytecode digital fingerprint associated with the bytecode module; identifying, among a plurality of stored application binary interface (ABI) definitions, an ABI definition having an ABI digital fingerprint that matches the bytecode digital fingerprint; and producing decoded transaction data by decoding, using the identified ABI definition, the transaction data.