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Philip Daian

Researcher at Cornell University

Publications -  21
Citations -  1258

Philip Daian is an academic researcher from Cornell University. The author has contributed to research in topics: Cryptocurrency & Formal semantics (linguistics). The author has an hindex of 10, co-authored 20 publications receiving 777 citations. Previous affiliations of Philip Daian include University of Illinois at Urbana–Champaign & Denso.

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Proceedings ArticleDOI

KEVM: A Complete Formal Semantics of the Ethereum Virtual Machine

TL;DR: KEVM is presented, an executable formal specification of the EVM's bytecode stack-based language built with the K Framework, designed to serve as a solid foundation for further formal analyses and to demonstrate the usability of the semantics.
Proceedings ArticleDOI

Flash Boys 2.0: Frontrunning in Decentralized Exchanges, Miner Extractable Value, and Consensus Instability

TL;DR: This work highlights the large, complex risks created by transaction-ordering dependencies in smart contracts and the ways in which traditional forms of financial-market exploitation are adapting to and penetrating blockchain economies.
Posted Content

Flash Boys 2.0: Frontrunning, Transaction Reordering, and Consensus Instability in Decentralized Exchanges.

TL;DR: This work highlights the large, complex risks created by transaction-ordering dependencies in smart contracts and the ways in which traditional forms of financial-market exploitation are adapting to and penetrating blockchain economies.

KEVM: A Complete Semantics of the Ethereum Virtual Machine

TL;DR: KEVM is presented, the first fully executable formal semantics of the EVM, the bytecode language in which smart contracts are executed, in a framework for executable semantics, the K framework, and it is shown that the approach is feasible and not computationally restrictive.
Journal Article

Tesseract: Real-Time Cryptocurrency Exchange using Trusted Hardware.

TL;DR: Tesseract as discussed by the authors is a secure real-time cryptocurrency exchange service that uses a trusted execution environment to commit the recent trade data to independent cryptocurrency systems, which is an all-or-nothing fairness problem.