  One of the defining features of Bitcoin and the thousands of cryptocurrencies
that have been derived from it is a globally visible transaction ledger. While
Bitcoin uses pseudonyms as a way to hide the identity of its participants, a
long line of research has demonstrated that Bitcoin is not anonymous. This has
been perhaps best exemplified by the development of clustering heuristics,
which have in turn given rise to the ability to track the flow of bitcoins as
they are sent from one entity to another.
  In this paper, we design a new heuristic that is designed to track a certain
type of flow, called a peel chain, that represents many transactions performed
by the same entity; in doing this, we implicitly cluster these transactions and
their associated pseudonyms together. We then use this heuristic to both
validate and expand the results of existing clustering heuristics. We also
develop a machine learning-based validation method and, using a ground-truth
dataset, evaluate all our approaches and compare them with the state of the
art. Ultimately, our goal is to not only enable more powerful tracking
techniques but also call attention to the limits of anonymity in these systems.
