Blockchain Oracle Design Patterns

TL;DR

This paper analyzes blockchain oracle design patterns, classifying them into voting-based and reputation-based strategies, to improve smart contract access to external data for enhanced blockchain functionality.

Contribution

It provides a structured classification and detailed pattern descriptions of blockchain oracles, highlighting research directions and novel insights into their mechanisms.

Findings

Classified blockchain oracles into voting-based and reputation-based groups.

Detailed description of oracle design patterns within each classification.

Discussed future research directions in blockchain oracle development.

Abstract

Blockchain is a form of distributed ledger technology (DLT) where data is shared among users connected over the internet. Transactions are data state changes on the blockchain that are permanently recorded in a secure and transparent way without the need of a third party. Besides, the introduction of smart contracts to the blockchain has added programmability to the blockchain and revolutionized the software ecosystem leading toward decentralized applications (DApps) attracting businesses and organizations to employ this technology. Although promising, blockchains and smart contracts have no access to the external systems (i.e., off-chain) where real-world data and events resides; consequently, the usability of smart contracts in terms of performance and programmability would be limited to the on-chain data. Hence, \emph{blockchain oracles} are introduced to mitigate the issue and are defined as trusted third-party services that send and verify the external information (i.e., feedback) and submit it to smart contracts for triggering state changes in the blockchain. In this paper, we will study and analyze blockchain oracles with regard to how they provide feedback to the blockchain and smart contracts. We classify the blockchain oracle techniques into two major groups such as voting-based strategies and reputation-based ones. The former mainly relies on participants' stakes for outcome finalization while the latter considers reputation in conjunction with authenticity proof mechanisms for data correctness and integrity. We then provide a structured description of patterns in detail for each classification and discuss research directions in the end.