Affiliation:
1. College of Computing, Prince of Songkla University, Phuket 83120, Thailand
2. Faculty of Technology and Environment, Prince of Songkla University, Phuket 83120, Thailand
Abstract
Smart contracts refer to small programs that run in a decentralized blockchain infrastructure. The blockchain system is trustless, and the determination of common variables is done by consensus between peers. Developing applications that require generating random variables becomes significantly challenging—for instance, lotteries, games, and random assignments. Many random number generators (RNGs) for smart contracts have been developed for the decentralized environment. The methods can be classified into three categories: on-chain RNG, Verifiable Random Function (VRF), and the Commit–reveal scheme. Although the existing methods offer different strengths and weaknesses, none achieves the three important requirements for an ideal RNG solution: security, applicability, and cost efficiency. This paper proposes a novel RNG approach called Native VRF, which offers application development simplicity and cost efficiency while maintaining strong RNG security properties. Experimental results show that Native VRF has the same security properties as the widely used RNG methods, i.e., Randao and Chainlink VRF. On top of that, our work offers a much simpler setup process and lower hardware resources and developer expertise requirements. Most importantly, the proposed Native VRF is compatible with all Ethereum virtual machine (EVM) blockchains, contributing to the overall growth of the blockchain ecosystem.
Funder
National Science, Research and Innovation Fund (NSRF) and Prince of Songkla University
College of Computing, Prince of Songkla University
Subject
Information Systems and Management,Computer Networks and Communications,Modeling and Simulation,Control and Systems Engineering,Software
Reference34 articles.
1. Bartoletti, M., and Pompianu, L. (2017, January 7). An Empirical Analysis of Smart Contracts: Platforms, Applications, and Design Patterns. Proceedings of the Financial Cryptography and Data Security, Sliema, Malta.
2. Azzolini, D., Riguzzi, F., and Lamma, E. (2020, January 8–10). Modeling Smart Contracts with Probabilistic Logic Programming. Proceedings of the International Conference on Business Information Systems, Colorado Springs, CO, USA.
3. Cusack, L. (2023, January 23). Pool Together. Available online: https://medium.com/pooltogether/pooltogether-101-eaf9b1b759dc.
4. Metav.rs (2022, December 19). NFT Market–Statistics 2021–2023. Available online: https://metav.rs/blog/nft-market-statistics-2021-2022.
5. Mohanta, B.K., Panda, S.S., and Jena, D. (2018, January 10–12). An overview of smart contract and use cases in blockchain technology. Proceedings of the 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Bengaluru, India.