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Characterizing Types of Smart Contracts in the Ethereum Landscape

  • Conference paper
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Financial Cryptography and Data Security (FC 2020)

Abstract

After cryptocurrencies, smart contracts are the second major innovation of the blockchain era. Leveraging the immutability and accountability of blockchains, these event-driven programs form the basis of the new digital economy with tokens, wallets, exchanges, and markets, but facilitating also new models of peer-to-peer organizations. To judge the long-term prospects of particular projects and this new technology in general, it is important to understand how smart contracts are used. While public announcements, by their nature, make promises of what smart contracts might achieve, openly available data of blockchains provides a more balanced view on what is actually going on.

We focus on Ethereum as the major platform for smart contracts and aim at a comprehensive picture of the smart contract landscape regarding common or heavily used types of contracts. To this end, we unravel the publicly available data of the main chain up to block 9 000 000, in order to obtain an understanding of almost 20 million deployed smart contracts and 1.5 billion interactions. As smart contracts act behind the scenes, their activities are only fully accessible by also considering the execution traces triggered by transactions. They serve as the basis for this analysis, in which we group contracts according to common characteristics, observe temporal aspects and characterize them quantitatively and qualitatively. We use static methods by analyzing the bytecode of contracts as well as dynamic methods by aggregating and classifying the communication between contracts.

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Notes

  1. 1.

    For the 76.5 k source codes from Etherscan, we observe 50 mismatches between the signatures extracted by our tool and the interface there. In all these cases our tool works actually correctly, whereas the given interface on Etherscan is inaccurate.

  2. 2.

    An infinity of possible function headers is mapped to a finite number of signatures, so there is no guarantee that we recover the original header. The probability of collisions is low, however. E.g., of the 328 k signatures in our dictionary, only 19 appear with a second function header.

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Correspondence to Monika di Angelo .

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di Angelo, M., Salzer, G. (2020). Characterizing Types of Smart Contracts in the Ethereum Landscape. In: Bernhard, M., et al. Financial Cryptography and Data Security. FC 2020. Lecture Notes in Computer Science(), vol 12063. Springer, Cham. https://doi.org/10.1007/978-3-030-54455-3_28

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  • DOI: https://doi.org/10.1007/978-3-030-54455-3_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-54454-6

  • Online ISBN: 978-3-030-54455-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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