CRJan 14, 2022Code
Zef: Low-latency, Scalable, Private PaymentsMathieu Baudet, Alberto Sonnino, Mahimna Kelkar et al.
We introduce Zef, the first Byzantine-Fault Tolerant (BFT) protocol to support payments in anonymous digital coins at arbitrary scale. Zef follows the communication and security model of FastPay: both protocols are asynchronous, low-latency, linearly-scalable, and powered by partially-trusted sharded authorities. Zef further introduces opaque coins represented as off-chain certificates that are bound to user accounts. In order to hide the face values of coins when a payment operation consumes or creates them, Zef uses random commitments and NIZK proofs. Created coins are made unlinkable using the blind and randomizable threshold anonymous credentials of Coconut. To control storage costs associated with coin replay prevention, Zef accounts are designed so that data can be safely removed once an account is deactivated. Besides the specifications and a detailed analysis of the protocol, we are making available an open-source implementation of Zef in Rust. Our extensive benchmarks on AWS confirm textbook linear scalability and demonstrate a confirmation time under one second at nominal capacity. Compared to existing anonymous payment systems based on a blockchain, this represents a latency speedup of three orders of magnitude, with no theoretical limit on throughput.
CRSep 9, 2021
Clockwork Finance: Automated Analysis of Economic Security in Smart ContractsKushal Babel, Philip Daian, Mahimna Kelkar et al.
We introduce the Clockwork Finance Framework (CFF), a general purpose, formal verification framework for mechanized reasoning about the economic security properties of composed decentralized-finance (DeFi) smart contracts. CFF features three key properties. It is contract complete, meaning that it can model any smart contract platform and all its contracts--Turing complete or otherwise. It does so with asymptotically constant model overhead. It is also attack-exhaustive by construction, meaning that it can automatically and mechanically extract all possible economic attacks on users' cryptocurrency across modeled contracts. Thanks to these properties, CFF can support multiple goals: economic security analysis of contracts by developers, analysis of DeFi trading risks by users, fees UX, and optimization of arbitrage opportunities by bots or miners. Because CFF offers composability, it can support these goals with reasoning over any desired set of potentially interacting smart contract models. We instantiate CFF as an executable model for Ethereum contracts that incorporates a state-of-the-art deductive verifier. Building on previous work, we introduce extractable value (EV), a new formal notion of economic security in composed DeFi contracts that is both a basis for CFF and of general interest. We construct modular, human-readable, composable CFF models of four popular, deployed DeFi protocols in Ethereum: Uniswap, Uniswap V2, Sushiswap, and MakerDAO, representing a combined 24 billion USD in value as of March 2022. We use these models along with some other common models such as flash loans, airdrops and voting to show experimentally that CFF is practical and can drive useful, data-based EV-based insights from real world transaction activity. Without any explicitly programmed attack strategies, CFF uncovers on average an expected $56 million of EV per month in the recent past.