Tracing Transactions Across Cryptocurrency Ledgers
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Abstract (arXiv)
One of the defining features of a cryptocurrency is that its ledger, containing all transactions that have evertaken place, is globally visible. As one consequenceof this degree of transparency, a long line of recent re-search has demonstrated that even in cryptocurrenciesthat are specifically designed to improve anonymity it is often possible to track money as it changes hands,and in some cases to de-anonymize users entirely. With the recent proliferation of alternative cryptocurrencies, however, it becomes relevant to ask not only whether ornot money can be traced as it moves within the ledgerof a single cryptocurrency, but if it can in fact be tracedas it moves across ledgers. This is especially pertinent given the rise in popularity of automated trading platforms such as ShapeShift, which make it effortless to carry out such cross-currency trades. In this paper, weuse data scraped from ShapeShift over a thirteen-monthperiod and the data from eight different blockchains to explore this question. Beyond developing new heuristics and creating new types of links across cryptocurrency ledgers, we also identify various patterns of cross-currency trades and of the general usage of these platforms, with the ultimate goal of understanding whetherthey serve a criminal or a profit-driven agenda.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Oct 2018.
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Citing papers per year: 2022: 2; 2023: 0; 2024: 1; 2025: 2; 2026: 1.
6 papers.
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- 2024Machine Learning for Blockchain Data Analysis: Progress and Opportunitiescited by 2
- 2025HyPV-LEAD: Proactive Early-Warning of Cryptocurrency Anomalies through Data-Driven Structural-Temporal Modelingcited by 2
- 2025Slow is Fast! Dissecting Ethereum's Slow Liquidity Drain Scamscited by 0
- 2026Population-Calibrated Graph Screening at 835-Million-Address Scale, with Label-Free Transfer to New Chainscited by 0
- 2026Population-Calibrated Graph Screening at 835-Million-Address Scale, with Label-Free Transfer to New Chains2 Sep 2026
- 2025HyPV-LEAD: Proactive Early-Warning of Cryptocurrency Anomalies through Data-Driven Structural-Temporal Modeling3 Sep 2025
- 2025Slow is Fast! Dissecting Ethereum's Slow Liquidity Drain Scams6 Mar 2025
- 2024Machine Learning for Blockchain Data Analysis: Progress and Opportunities28 Apr 2024
- 2022Anomaly Detection in Multiplex Dynamic Networks: from Blockchain Security to Brain Disease Prediction15 Nov 2022
- 2022BABD: A Bitcoin Address Behavior Dataset for Pattern Analysis10 Apr 2022