Proposed and analyzed hints for MEV-Share and their use cases in order to enable easier backrunning-based MEV extractions.
Used Laplace noise to create Differentially Private aggregate hints (of 3 different types) and discussed its effects on backrunning strategy.
Education
PhD Computer Science
Duke University
PhD candidate in Computer Science at Duke University. Studying security of blockchains and cryptocurrency. Making DeFi fast and Efficient. Currently working with Ethereum Foundation on an Inclusion List Design - AUCIL and separately on analyzing Efficacy of Preconfs.
Integrated B. Tech. and M. Tech. in Computer Science and Engineering