Towards Measuring the Traceability of Cryptocurrencies

TL;DR

This paper introduces a novel, quantitative framework using Markov chains and Shannon entropy to measure the traceability and privacy of cryptocurrencies, enabling better understanding of their anonymity guarantees.

Contribution

It presents the first practical, probabilistic method to quantify cryptocurrency traceability, applicable to entire transaction graphs, advancing privacy analysis tools.

Findings

Bitcoin shows more natural mixing than Ethereum in one-week analysis.

The proposed measure is efficient and generalizes to entire transaction graphs.

Provides a new quantitative basis for assessing cryptocurrency privacy.

Abstract

Cryptocurrencies aim to replicate physical cash in the digital realm while removing centralized and trusted intermediaries. Decentralization is achieved by the blockchain, a permanent public ledger that contains a record of every transaction. The public ledger ensures transparency, which enables public verifiability but harms untraceability, fungibility, and anonymity. In the last decade, cryptocurrencies attracted millions of users, with their total market cap reaching approximately three trillion USD at its peak. However, their anonymity guarantees are poorly understood and plagued by widespread misbeliefs. Indeed, previous notions of privacy, anonymity, and traceability for cryptocurrencies are either non-quantitative or inapplicable, e.g., computationally hard to measure. In this work, we put forward a formal framework to measure the (un)traceability and anonymity of cryptocurrencies, allowing us to quantitatively reason about the mixing characteristics of cryptocurrencies and the privacy-enhancing technologies built on top of them. Our methods apply absorbing Markov chains combined with Shannon entropy. To the best of our knowledge, our work provides the first practical, efficient, and probabilistic measure to assess the traceability of cryptocurrencies quantitatively, which also generalizes to entire cryptocurrency transaction graphs. We implement and extensively evaluate our proposed traceability measure on several cryptocurrency transaction graphs. Among other quantitative results, we find that in the studied one-week interval, the Bitcoin blockchain, on average, provided comparable but quantifiably more natural mixing than the Ethereum blockchain.