Blockchain-based vs. SQL Database Systems for Digital Twin Evidence Management: A Comparative Forensic Analysis

2025-11-29

AI Summary

This study addresses the challenges of evidence integrity and verifiability in digital forensics within metaverse and digital twin environments. Methodologically, it conducts the first systematic comparative evaluation of blockchain (Ethereum + IPFS) versus traditional SQL databases for digital twin evidence management, employing hash-chain verification and controlled experiments to assess data integrity, write throughput, and query latency. Results demonstrate that blockchain significantly enhances evidentiary immutability and long-term integrity assurance, achieving higher average storage speed; however, it exhibits substantially greater query latency variability and lower retrieval stability than SQL databases. The study proposes a metaverse-oriented forensic evidence modeling approach and a layered evidence preservation framework, clarifying the applicability boundaries and trade-off mechanisms of blockchain in judicially trusted systems. These findings provide both theoretical foundations and empirical validation for building high-assurance digital forensic infrastructure.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Search and Optimization: Metareasoning and Metaheuristics
• Multiagent Systems: Other Foundations of Multi Agent Systems

Application Category

• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Security and Privacy: Blockchains and distributed ledgers
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data

Abstract

Digital forensics faces unprecedented challenges with the emergence of digital twins and metaverse technologies. This paper presents the first comparative analysis between blockchain-based and traditional database systems for managing digital twin evidence in forensic investigations. We conducted controlled experiments comparing the Ethereum blockchain with IPFS storage against traditional SQL databases for digital twin evidence management. Our findings reveal that while blockchain provides superior data integrity and immutability, crucial for forensic applications, traditional databases offer better performance consistency. The blockchain implementation showed faster average storage times but higher variability in retrieval operations. Both systems maintained forensic integrity through hash verification, though blockchain's immutable nature provides additional security guarantees essential for legal proceedings. This research contributes to the development of robust digital forensic methodologies for emerging technologies in the metaverse era.

Problem

• Compares blockchain and SQL databases for digital twin evidence management
• Evaluates data integrity versus performance consistency in forensic systems
• Assesses storage and retrieval characteristics for metaverse forensic investigations

Innovation

• Blockchain ensures data integrity and immutability for forensics
• SQL databases provide better performance consistency
• Both systems use hash verification for forensic integrity