Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

4-2023

Abstract

Ethereum has become a popular blockchain with smart contracts for investors nowadays. Due to the decentralization and anonymity of Ethereum, Ponzi schemes have been easily deployed and caused significant losses to investors. However, there are still no explainable and effective methods to help investors easily identify Ponzi schemes and validate whether a smart contract is actually a Ponzi scheme. To fill the research gap, we propose PonziLens, a novel visualization approach to help investors achieve early identification of Ponzi schemes by investigating the operation codes of smart contracts. Specifically, we conduct symbolic execution of opcode and extract the control flow for investing and rewarding with critical opcode instructions. Then, an intuitive directed-graph based visualization is proposed to display the investing and rewarding flows and the crucial execution paths, enabling easy identification of Ponzi schemes on Ethereum. Two usage scenarios involving both Ponzi and non-Ponzi schemes demonstrate the effectiveness of PonziLens.

Keywords

Blockchain, crypto-currency, on-chain data analysis, Ethereum

Discipline

Databases and Information Systems | Programming Languages and Compilers

Research Areas

Data Science and Engineering

Publication

CHI’23 Extended Abstract Proceedings, Hamburg, Germany, 2023 April 23-28

First Page

1

Last Page

6

ISBN

9781450394222

Identifier

10.1145/3544549.3585861

Publisher

CHI EA '23: Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems

City or Country

https://doi.org/10.1145/3544549.3585861

Additional URL

https://doi.org/10.1145/3544549.3585861

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