ACon$^2$: Adaptive Conformal Consensus for Provable Blockchain Oracles
This addresses security vulnerabilities in blockchain smart contracts by providing a provable solution to the oracle problem, though it is incremental as it builds on existing uncertainty quantification methods.
The paper tackles the oracle problem in blockchains, where a single oracle contract introduces a failure risk, by proposing an adaptive conformal consensus algorithm that derives a consensus set from multiple oracles with correctness guarantees under distribution shift and Byzantine adversaries, demonstrating efficacy on price datasets and an Ethereum case study.
Blockchains with smart contracts are distributed ledger systems that achieve block-state consistency among distributed nodes by only allowing deterministic operations of smart contracts. However, the power of smart contracts is enabled by interacting with stochastic off-chain data, which in turn opens the possibility to undermine the block-state consistency. To address this issue, an oracle smart contract is used to provide a single consistent source of external data; but, simultaneously, this introduces a single point of failure, which is called the oracle problem. To address the oracle problem, we propose an adaptive conformal consensus (ACon$^2$) algorithm that derives a consensus set of data from multiple oracle contracts via the recent advance in online uncertainty quantification learning. Interesting, the consensus set provides a desired correctness guarantee under distribution shift and Byzantine adversaries. We demonstrate the efficacy of the proposed algorithm on two price datasets and an Ethereum case study. In particular, the Solidity implementation of the proposed algorithm shows the potential practicality of the proposed algorithm, implying that online machine learning algorithms are applicable to address security issues in blockchains.