Development of Domain-Specific Languages for smart contracts
Security analysis of smart contracts and blockchain-based applications
Blockchain-based monetary fraud analysis and detection
Custom data analysis on blockchain (de-anonymisation, attack patterns, ...)
Design and development of blockchain-based applications (on Bitcoin, Ethereum, ...)
Business consultancy on blockchain technologies and cyber-security
Journal of Grid Computing, 2019
Besides recording transfers of currency, the Bitcoin blockchain is being used to save metadata — i.e. arbitrary pieces of data which do not affect transfers of bitcoins. This can be done by using different techniques, and for different purposes. For instance, a growing number of protocols embed metadata in the blockchain to certify and transfer the ownership of a variety of assets beyond cryptocurrency. A point of debate in the Bitcoin community is whether metadata negatively impact on the effectiveness of Bitcoin with respect to its primary function. This paper is a systematic analysis of the usage of Bitcoin metadata over the years. We discuss all the known techniques to embed metadata in the Bitcoin blockchain; we then extract metadata, and analyse them from different angles.
Financial Cryptography and Data Security, 2018.
We propose a formal model of Bitcoin transactions, which is sufficiently abstract to enable formal reasoning, and at the same time is concrete enough to serve as an alternative documentation to Bitcoin. We use our model to formally prove some well-formedness properties of the Bitcoin blockchain, for instance that each transaction can only be spent once. We release an open-source tool through which programmers can write transactions in our abstract model, and compile them into standard Bitcoin transactions.
Principles of Security and Trust, 2018.
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.
Principles of Security and Trust, 2017
Smart contracts are computer programs that can be correctly executed by a network of mutually distrusting nodes, without the need of an external trusted authority. Since smart contracts handle and transfer assets of considerable value, besides their correct execution it is also crucial that their implementation is secure against attacks which aim at stealing or tampering the assets. We study this problem in Ethereum, the most well-known and used framework for smart contracts so far. We analyse the security vulnerabilities of Ethereum smart contracts, providing a taxonomy of common programming pitfalls which may lead to vulnerabilities. We show a series of attacks which exploit these vulnerabilities, allowing an adversary to steal money or cause other damage.
Crypto Valley Conference on Blockchain Technology, 2018
Soon after its introduction in 2009, Bitcoin has been adopted by cyber-criminals, which rely on its pseudonymity to implement virtually untraceable scams. One of the typical scams that operate on Bitcoin are the so-called Ponzi schemes. These are fraudulent investments which repay users with the funds invested by new users that join the scheme, and implode when it is no longer possible to find new investments. Despite being illegal in many countries, Ponzi schemes are now proliferating on Bitcoin, and they keep alluring new victims, who are plundered of millions of dollars. We apply data mining techniques to detect Bitcoin addresses related to Ponzi schemes. Our starting point is a dataset of features of real-world Ponzi schemes, that we construct by analysing, on the Bitcoin blockchain, the transactions used to perform the scams. We use this dataset to experiment with various machine learning algorithms, and we assess their effectiveness through standard validation protocols and performance metrics. The best of the classifiers we have experimented can identify most of the Ponzi schemes in the dataset, with a low number of false positives.
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 Ethereum - currently 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.