Map-Z: Exposing the Zcash Network in Times of Transition
This work addresses the problem of understanding network topology for privacy-preserving cryptocurrencies like Zcash, with broader implications for topology hiding in other cryptocurrencies, though it is incremental in applying existing timing analysis methods.
The paper tackled the lack of information on the Zcash network by conducting the first long-term measurement study, capturing metrics like network size and centralization, and developed a topology inference method with 50% precision and 82% recall in real-world experiments.
Zcash is a privacy-preserving cryptocurrency that provides anonymous monetary transactions. While Zcash's anonymity is part of a rigorous scientific discussion, information on the underlying peer-to-peer network are missing. In this paper, we provide the first long-term measurement study of the Zcash network to capture key metrics such as the network size and node distribution as well as deeper insights on the centralization of the network. Furthermore, we present an inference method based on a timing analysis of block arrivals that we use to determine interconnections of nodes. We evaluate and verify our method through simulations and real-world experiments, yielding a precision of 50 % with a recall of 82 % in the real-world scenario. By adjusting the parameters, the topology inference model is adaptable to the conditions found in other cryptocurrencies and therefore also contributes to the broader discussion of topology hiding in general.