DIA Multi-Randomness: Distributed Multi-chain Randomness Oracle

DIA Multi-Randomness, in collaboration with drand, provides smart contracts with unpredictable and unbiased random numbers, facilitating the development of Web3 use cases across multiple blockchains.

July 27, 2022


Unpredictable random number generation has become an essential building block for Web3. Decentralised applications, ranging from Web3 games to NFT collections or prediction markets, rely on random numbers to operate. For instance, a random number can grant a game with a lottery mechanism to reward players fairly or provide unpredictable attributes to a collection of NFT artworks.

Providing a source of randomness on-chain is a difficult task due to two main aspects. On one hand, it is very difficult to create random events on-chain due to the deterministic nature of any EVM environment. On the other hand, if the random number generation comes from a centralised source, malicious actors can manipulate the randomness from the source, and provoke irreversible damage to protocols and users.

In order to effectively serve the needs of Web3 applications, random number generation must be tamper-proof, distributed, publicly verifiable and available on multiple blockchain networks.

Introducing DIA Multi-Randomness

We are delighted to present a new addition to the DIA oracle suite: DIA Multi-Randomness — verifiable and distributed multi-chain randomness oracle. In close collaboration with drand, DIA Multi-Randomness will provide dApps with an auditable random number via oracle smart contract, available in 20+ L1s and L2s.

DIA Multi-Randomness tackles essential Web3 development needs:

  • Multi-chain compatibility: DIA’s Multi-Randomness can be easily deployed in all EVM-compatible networks served by DIA including Astar, Arbitrum, Fantom and many more.
  • Verifiable randomness: all random values provided by DIA are verifiable off-chain, before becoming available to consuming smart contracts and hence, the oracle is not capable of manipulating the generated result.
  • Distributed and decentralised: DIA’s Multi-Randomness leverages drand, a distributed beacon generated by a network of participants named the League of Entropy — LoE. As drand is not singularly controlled by any of its members, there is no single point of failure and none of the operators themselves can bias the randomness generated by the League.
  • Power your on-chain use cases: DIA’s Multi-Randomness can support a wide range of Web3 use cases including gaming, lotteries, prediction markets, NFT launches and many more.

How Does it Work?

To offer distributed and verifiable randomness on-chain, DIA leverages drand’s distributed randomness beacon, enabling verifiable, unpredictable and unbiased random numbers. From random number generation to on-chain distribution, DIA Multi-Randomness operates as follows:

  1. Drand randomness generation: in every drand epoch (currently set at 30s for the existing LoE beacons, but planned to be decreased on future “unchained” beacons) each of the nodes that form the drand network generates a partial signature, which it broadcasts to the rest of the nodes. Once any node has enough, i.e., a threshold number of signatures, it computes the new randomness beacon, which is the hash of the signature aggregate.
  2. Drand randomness propagation and consumption: the new beacon is propagated through the network to all other nodes. Any node can verify and accept or reject the hash of the signature (i.e., beacon) that it received. Any client or application can consume randomness out of band (i.e., without needing to be part of the drand network) either through the public HTTP APIs or through libp2p’s pubsub protocol, Gossipsub. Clients can also verify that the randomness they received is indeed the one produced by the drand network.
  3. DIA on-chain distribution: the final randomness signature is shipped on-chain as an oracle smart contract. In order to effectively and securely execute these on-chain transactions, DIA has built a robust, decentralised node infrastructure — dubbed DIA xNode. DIA xNode is a network of third-party, decentralized node providers that grant DIA the blockchain infrastructure to push data on-chain ensuring high rate limits.

Sid Sanyal, drand Project Lead at Protocol Labs comments:

“Being able to provide publicly verifiable, and non-biasable on-chain randomness is a significant challenge for the Web3.0 space. DIA’s oracles that integrate drand randomness is a huge first step in that direction. We are very excited about this development, and it has been a pleasure to work with the DIA team.”

Powering on-chain use cases

DIA’s Multi-Randomness can support a wide range of Web3 use cases, such as:

  • On-chain lottery: due to the deterministic nature of blockchains, decentralised applications require trustless and tamper-proof on-chain random numbers to execute lotteries.
  • Randomized NFTs: a random number can enable the randomised assignment of characteristics and attributes for non-fungible tokens
  • On-chain gaming: DIA’s multi-randomness can be used to generate unpredictable scenarios and rewards, creating engaging experiences for on-chain games.

Request a DIA Multi-Randomness Oracle for your dApp

Are you building a decentralised application that requires a reliable source of randomness on-chain? Get in touch with the DIA team via Discord to request a custom oracle for your use cases.

→ https://go.diadata.org/Dev-Discord

Learn more

Visit the DIA Documentation to learn more about DIA Multi-Randomness and how to integrate it into your smart contract. Additionally, DIA offers demo Multi-Randomness oracles in various blockchain networks for developers to test in live environments.

Randomness Oracle

This article describes usage and deployment addresses of the DIA randomness oracle.

docs.diadata.org (Randomness Oracle - DIA Documentation)

Visit the drand Documentation page to learn the drand protocol internals, as well as how to consume randomness from the drand network.


This section introduces the key concepts behind drand. For information about consuming the randomness from a drand…