A Survey of Transaction Tracing Techniques for Blockchain Systems

TL;DR

This paper addresses the challenge of tracing blockchain transactions, including cross-chain flows, to curb illicit activity. It adopts a systematic literature review to classify tracing techniques into heuristic-based, rule-based, and graph learning-based approaches, organized by objectives such as deriving insights, improving detection, and speeding up queries. The authors compile a taxonomy, analyze datasets, and highlight limitations (e.g., cross-chain privacy, lack of standard benchmarks) while offering directions like temporal graph encoding and open-source data sharing. The work provides a structured foundation for researchers and practitioners to design robust cross-chain tracing solutions with an emphasis on practical applicability and reproducibility.

Abstract

With the proliferation of new blockchain-based cryptocurrencies/assets and platforms that make it possible to transact across them, it becomes important to consider not just whether the transfer of coins/assets can be tracked within their respective transaction ledger, but also if they can be tracked as they move across ledgers. This is especially important given that there are documented cases of criminals attempting to use these cross-ledger trades to obscure the flow of their coins/assets. In this paper, we perform a systematic review of the various tracing techniques for blockchain transactions proposed in literature, categorize them using multiple criteria (such as tracing approach and targeted objective) and compare them. Based on the above categorization, we provide insights on the state of blockchain transaction tracing literature and identify the limitations of existing approaches. Finally, we suggest directions for future research in this area based on our analysis.

Figures (1)

• Figure 1: The cross-ledger transaction flow model cltracer.