Permissionless blockchains promise resilience against censorship by a single entity. This suggests that deterministic rules, not third-party actors, decide whether a transaction is appended to the blockchain. In 2022, the U.S. ØFAC sanctioned a Bitcoin mixer and an Ethereum application, challenging the neutrality of permissionless blockchains.

Privacy within the Bitcoin ecosystem has been critical for the operation and propagation of the system since its very first release. While various entities have sought to deanonymize and reveal user identities, the default semi-anonymous approach to privacy was judged as insufficient and the community developed a number of advanced privacy-preservation mechanisms. In this study, we propose an improved variant of the multiple-input clustering approach that incorporates advanced privacy-enhancing techniques. We examine the CoinJoin-adjusted user graph of Bitcoin through quantitative network analysis and draw conclusions on the effectiveness of our proposed clustering method compared to naive multiple-input clustering. Our findings indicate that CoinJoin transactions can significantly distort commonly applied address clustering approaches. Moreover, we demonstrate that Bitcoin's user graph has become less dense in recent years, concurrent with the collapse of several independent user clusters. Our results contribute to a more comprehensive understanding of privacy aspects in the Bitcoin transaction network and lay the groundwork for developing enhanced measures to prevent money laundering and terrorism financing.

Stealth addresses represent an approach to enhancing privacy within public and distributed blockchains, such as Ethereum and Bitcoin. Stealth address protocols employ a distinct, randomly generated address for the recipient, thereby concealing interactions between entities. In this study, we introduce BaseSAP, an autonomous base-layer protocol for embedding stealth addresses within the application layer of programmable blockchains. BaseSAP expands upon previous research to develop a modular protocol for executing unlinkable transactions on public blockchains. BaseSAP allows for the development of additional stealth address layers using different cryptographic algorithms on top of the primary implementation, capitalizing on its modularity. To demonstrate the effectiveness of our proposed protocol, we present simulations of an advanced Secp256k1-based dual-key stealth address protocol. This protocol is developed on top of BaseSAP and deployed on the Ethereum test network as the first prototype implementation. Furthermore, we provide cost analyses and underscore potential security ramifications and attack vectors that could affect the privacy of stealth addresses. Our study highlights the flexibility of the BaseSAP protocol and provides insights into the broader implications of stealth address technology in the realm of blockchain privacy.

The potential of Bitcoin for money laundering and terrorist financing represents a significant challenge in law enforcement. In recent years, the use of privacy-improving CoinJoin transactions has grown significantly and helped criminal actors obfuscate Bitcoin money flows. In this study, we use unsupervised machine learning to analyze the complete Bitcoin user graph in order to identify suspicious actors potentially involved in illegal activities. In contrast to the existing studies, we introduce a novel set of features that we use to identify potential criminal activity more accurately. Furthermore, we apply our clustering algorithm to a CoinJoin-adjusted variant of the Bitcoin user graph, which enables us to analyze the network at a more detailed, user-centric level while still offering opportunities to address advanced privacy-enhancing techniques at a later stage. By comparing the results with our ground truth data set, we find that our improved clustering method is able to capture significantly more illicit activity within the most suspicious clusters. Finally, we find that users associated with illegal activities commonly have significant short paths to CoinJoin wallets and show tendencies toward outlier behavior. Our results have potential contributions to anti-money laundering efforts and combating the financing of terrorism and other illegal activities.