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In August of 2022, the United States Department of Treasury sanctioned the virtual currency mixer Tornado Cash, an open-source and fully decentralised piece of software running on the Ethereum blockchain, subsequently leading to the arrest of one of its developers in the Netherlands. Not only was this the first time the Office of Foreign Assets Control (OFAC) extended its authority to sanction a foreign 'person' to software, but the decentralised nature of the software and global usage highlight the challenge of establishing jurisdiction over decentralised software and its global user base. The government claims jurisdiction over citizens, residents, and any assets that pass through the country's territory. As a global financial center with most large tech companies, this often facilitates the establishment of jurisdiction over global conduct that passes through US servers. However, decentralised programs on blockchains with nodes located around the world challenge this traditional approach as either nearly all countries can claim jurisdiction over users, subjecting users to criminal laws in countries with which they have no true interaction, or they limit jurisdiction, thereby risking abuse by bad actors. This article takes a comparative approach to examine the challenges to establishing criminal jurisdiction on cryptocurrency-related crimes. [ABSTRACT FROM AUTHOR]

Bitcoin and Ethereum blockchains are exploding with data. While blockchain is becoming increasingly popular, the sheer volume of data obscures important concerns about safety, and privacy. Data analysis can aid in identifying problems and recommending possible solutions, and is also essential to make blockchain useful in a wide range of industries. The paper reviews the current state of research in four key areas: security, privacy, performance and price prediction, and compiles relevant literature. Finally, it predicts future trends and challenges in this field, which will serve as a roadmap for researchers. Blockchain data analysis will increasingly rely on Machine Learning. [ABSTRACT FROM AUTHOR]