  This thesis presents techniques to investigate transactions in uncharted
cryptocurrencies and services. Cryptocurrencies are used to securely send
payments online. Payments via the first cryptocurrency, Bitcoin, use
pseudonymous addresses that have limited privacy and anonymity guarantees.
Research has shown that this pseudonymity can be broken, allowing users to be
tracked using clustering and tagging heuristics. Such tracking allows crimes to
be investigated. If a user has coins stolen, investigators can track addresses
to identify the destination of the coins. This, combined with an explosion in
the popularity of blockchain, has led to a vast increase in new coins and
services. These offer new features ranging from coins focused on increased
anonymity to scams shrouded as smart contracts. In this study, we investigated
the extent to which transaction privacy has improved and whether users can
still be tracked in these new ecosystems. We began by analysing the
privacy-focused coin Zcash, a Bitcoin-forked cryptocurrency, that is considered
to have strong anonymity properties due to its background in cryptographic
research. We revealed that the user anonymity set can be considerably reduced
using heuristics based on usage patterns. Next, we analysed cross-chain
transactions collected from the exchange ShapeShift, revealing that users can
be tracked as they move across different ledgers. Finally, we present a
measurement study on the smart-contract pyramid scheme Forsage, a scam that
cycled $267 million USD (of Ethereum) within its first year, showing that at
least 88% of the participants in the scheme suffered a loss. The significance
of this study is the revelation that users can be tracked in newer
cryptocurrencies and services by using our new heuristics, which informs those
conducting investigations and developing these technologies.
