Conference paper · Book chapter
Maximizing Extractable Value from Automated Market Makers
In
Lecture Notes in Computer Science
—
2022, pp. 3-19
Abstract
Automated Market Makers (AMMs) are decentralized applications that allow users to exchange crypto-tokens without the need for a matching exchange order. AMMs are one of the most successful DeFi use cases: indeed, major AMM platforms process a daily volume of transactions worth USD billions. Despite their popularity, AMMs are well-known to suffer from transaction-ordering issues: adversaries can influence the ordering of user transactions, and possibly front-run them with their own, to extract value from AMMs, to the detriment of users.
We devise an effective procedure to construct a strategy through which an adversary can maximize the value extracted from user transactions.
| Language: | English |
|---|---|
| Publisher: | Springer |
| Year: | 2022 |
| Pages: | 3-19 |
| Proceedings: | 25th International Conference on Medical Image Computing and Computer-Assisted Intervention |
| Series: | Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| ISBN: | 3031182820 , 3031182839 , 9783031182822 and 9783031182839 |
| ISSN: | 16113349 and 03029743 |
| Types: | Conference paper and Book chapter |
| DOI: | 10.1007/978-3-031-18283-9_1 |
| ORCIDs: | Chiang, James Hsin yu and Lluch Lafuente, Alberto |
