No abstract

Modern cryptocurrencies exploit decentralised blockchains to record a public and unalterable history of transactions. Besides transactions, further information is stored for different, and often undisclosed, purposes, making the blockchains a rich and increasingly growing source of valuable information, in part of difficult interpretation. Many data analytics have been developed, mostly based on specifically designed and ad-hoc engineered approaches. We propose a general-purpose framework, seamlessly supporting data analytics on both Bitcoin and Ethereumcurrently the two most prominent cryptocurrencies. Such a framework allows us to integrate relevant blockchain data with data from other sources, and to organise them in a database, either SQL or NoSQL. Our framework is released as an open-source Scala library. We illustrate the distinguishing features of our approach on a set of significant use cases, which allow us to empirically compare ours to other competing proposals, and evaluate the impact of the database choice on scalability.

Albeit the primary usage of Bitcoin is to exchange currency, its blockchain and consensus mechanism can also be exploited to securely execute some forms of smart contracts. These are agreements among mutually distrusting parties, which can be automatically enforced without resorting to a trusted intermediary. Over the last few years a variety of smart contracts for Bitcoin have been proposed, both by the academic community and by that of developers. However, the heterogeneity in their treatment, the informal (often incomplete or imprecise) descriptions, and the use of poorly documented Bitcoin features, pose obstacles to the research. In this paper we present a comprehensive survey of smart contracts on Bitcoin, in a uniform framework. Our treatment is based on a new formal specification language for smart contracts, which also helps us to highlight some subtleties in existing informal descriptions, making a step towards automatic verification. We discuss some obstacles to the diffusion of smart contracts on Bitcoin, and we identify the most promising open research challenges.

We present a toolchain for developing and verifying smart contracts that can be executed on Bitcoin. The toolchain is based on BitML, a recent domain-specific language for smart contracts with a computationally sound embedding into Bitcoin. Our toolchain automatically verifies relevant properties of contracts, among which liquidity, ensuring that funds do not remain frozen within a contract forever. A compiler is provided to translate BitML contracts into sets of standard Bitcoin transactions: executing a contract corresponds to appending these transactions to the blockchain. We assess our toolchain through a benchmark of representative contracts.Demo Video URL: https://youtu.be/bxx3bM5Pm6c CCS CONCEPTS• Software and its engineering → Software verification; • Security and privacy → Distributed systems security.

Since the inception of Bitcoin in 2009, the market of cryptocurrencies has grown beyond the initial expectations, as witnessed by the thousands of tokenised assets available on the market, whose daily trades amount to dozens of USD billions. The pseudonymity features of these cryptocurrencies have attracted the attention of cybercriminals, who exploit them to carry out potentially untraceable scams. The wide range of cryptocurrency-based scams observed over the last ten years has fostered the research on the analysis of their effects, and the development of techniques to counter them. However, doing research in this field requires addressing several challenges: for instance, although a few data sources about cryptocurrency scams are publicly available, they often contain incomplete or misclassified data. Further, there is no standard taxonomy of scams, which leads to ambiguous and incoherent interpretations of their nature. Indeed, the unavailability of reliable datasets makes it difficult to train effective automatic classifiers that can detect and analyse cryptocurrency scams. In this paper, we perform an extensive review of the scientific literature on cryptocurrency scams, which we systematise according to a novel taxonomy. By collecting and homogenising data from different public sources, we build a uniform dataset of thousands of cryptocurrency scams. We devise an automatic tool that recognises scams and classifies them according to our taxonomy. We assess the effectiveness of our tool through standard performance metrics. We also give an in-depth analysis of the classification results, offering several insights into threat types, from their features to their connection with other types. Finally, we provide a set of guidelines that policymakers could follow to improve user protection against cryptocurrency scams.