An exploratory study of smart contracts in the Ethereum blockchain platform

Oliva, Gustavo Ansaldi; Hassan, Ahmed E.; Jiang, Zhen Ming

doi:10.1007/s10664-019-09796-5

Cited by 132 publications

(68 citation statements)

References 13 publications

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“…Until 2018, smart contracts in Ethereum are only used to develop simple tokencentric applications, e.g., ICOs and crowdsales. Eighty percents of the smart contracts use at most 211 instructions [101]. Smart contract code similarity reveals substantial code reuse, where less than 10% of user-created contracts are unique, and less than 1% of contract-created contracts are so [102].…”

Section: Other Collective Patternsmentioning

confidence: 99%

“…Moreover, contracts are three times more likely to be created by other contracts than they are by users [102]. As a result, 0.05% of the smart contracts are the target of 80% of the transactions [101], and that over 60% of contracts have never been interacted with [102]. Pinna et al [103] surveyed 10 000 smart contracts source codes and a dataset of meta-data from Etherscan.io.…”

Section: Other Collective Patternsmentioning

confidence: 99%

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Knowledge Discovery in Cryptocurrency Transactions: A Survey

et al. 2021

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Cryptocurrencies gain trust in users by publicly disclosing the full creation and transaction history. In return, the transaction history faithfully records the whole spectrum of cryptocurrency user behaviors. This article analyzes and summarizes the existing research on knowledge discovery in the cryptocurrency transactions using data mining techniques. Specifically, we classify the existing research into three aspects, i.e., transaction tracings and blockchain address linking, the analyses of collective user behaviors, and the study of individual user behaviors. For each aspect, we present the problems, summarize the methodologies, and discuss major findings in the literature. Furthermore, an enumeration of transaction data parsing and visualization tools and services is also provided. Finally, we outline several gaps and trends for future investigation in this research area.

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Section: Other Collective Patternsmentioning

confidence: 99%

Section: Other Collective Patternsmentioning

confidence: 99%

Knowledge Discovery in Cryptocurrency Transactions: A Survey

et al. 2021

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“…There are different types of blockchain platforms covering Hyper Ledger, Ethereum, and EOS for various application development types. 27,28 The blockchain platform is categorized as public, private, and consortium blockchain. Different features of the blockchain platform are as follows:…”

Section: Blockchain-based Systemmentioning

confidence: 99%

“…Initially, it was used in the cryptocurrency system for recording transition, for example, Bitcoin. There are different types of blockchain platforms covering Hyper Ledger, Ethereum, and EOS for various application development types 27,28 . The blockchain platform is categorized as public, private, and consortium blockchain.…”

Section: Preliminariesmentioning

confidence: 99%

Secure decision tree twin support vector machine training and classification process for encrypted IoT data via blockchain platform

Dey

Chaulya

Kumar

2021

Concurrency and Computation

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A secure decision tree twin support vector machine (DT-TSVM) multi-classification algorithm has been proposed in this paper for improving the reliability and security of the collected IoT data from multiple data providers. The multiclass secure DT-TSVM algorithm has been employed to train a machine learning model using the encrypted training dataset. The training dataset is collected via a blockchain platform.A blockchain method has been adopted to construct a secure and reliable distributed platform among dataset providers. The Paillier homomorphic cryptosystem has been applied for encrypting the IoT dataset. Then, the dataset has been recorded on the distributed ledger. The secure DT-TSVM algorithm's-based train model effectiveness has been compared with the other two available algorithms, namely the multiclass binary support vector machine (MBSVM) and one-to-one SVM algorithms. The experiment results showed that the privacy-preserving multiclass secure DT-TSVM-based model did not reduce the accuracy, but it increased the average precision and recall by 0.53% and 0.44% than MBSVM and 0.82% and 0.71% than one-to-one SVM, respectively. Further, the time consumption of data providers and data analysts did not change significantly with the increase of number of data provider.

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“…CLS refers to the time, measured in days, between the contract's deployment and its last execution. Most smart contracts live between 2 and 800 days [16], and the average age of smart contracts is 295.6 days [28]. A contract can be short-lived, medium-lived, or longlived [28], [16].…”

Section: B Measurement Of Negative Consequences Of Design Vulnerability In Smart Contractsmentioning

confidence: 99%

Proceedings of the 3rd International Conference on Technical Debt

2020

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An exploratory study of smart contracts in the Ethereum blockchain platform

Cited by 132 publications

References 13 publications

Knowledge Discovery in Cryptocurrency Transactions: A Survey

Knowledge Discovery in Cryptocurrency Transactions: A Survey

Secure decision tree twin support vector machine training and classification process for encrypted IoT data via blockchain platform

Proceedings of the 3rd International Conference on Technical Debt

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