Name
Abstract | ||
|---|---|---|
Blockchain users are identified by addresses (public keys), which cannot be easily linked back to them without out-of-network information. This provides pseudo-anonymity, which is amplified when the user generates a new address for each transaction. Since all transaction history is visible to all users in public blockchains, finding affiliation between related addresses can hurt pseudo-anonymity. Such affiliation information can be used to discriminate against addresses that were found to be related to a specific group, or can even lead to the de-anonymization of all addresses in the associated group, if out-of-network information is available on a few addresses in that group. In this work we propose to leverage a stylometry approach on Ethereum's deployed smart contracts' bytecode and high level source code, which is publicly available by third party platforms. We explore the extent to which a deployed smart contract's source code can contribute to the affiliation of addresses. To address this, we prepare a dataset of real-world Ethereum smart contracts data, which we make publicly available; design and implement feature selection, extraction techniques, data refinement heuristics, and examine their effect on attribution accuracy. We further use these techniques to test the classification of real-world scammers data. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.23919/CNSM46954.2019.9012681 | 2019 15th International Conference on Network and Service Management (CNSM) |
Keywords | DocType | ISSN |
blockchain,Ethereum,smart contracts,distributed ledger,traceability,authorship attribution | Conference | 2165-9605 |
ISBN | Citations | PageRank |
978-1-7281-5396-4 | 0 | 0.34 |
References | Authors | |
13 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shlomi Linoy | 1 | 1 | 2.06 |
| Natalia Stakhanova | 2 | 336 | 27.48 |
| Alina Matyukhina | 3 | 0 | 0.68 |