Datum

Web3

Privacy-preserving

Data Service

Enable secure and reliable data services and APIs for the Web3 ecosystem.

Empower data storage, data sharing, data collaboration, and data lifecycle

services while solving privacy-preserving concerns. Provide data oracle

service to feed off-chain data on-chain.

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Web3 Data Service Market

Realize 0 to 1 data lifecycle process, provide Web3 application experience and service market.

All services provide external APIs to integrate with Dapp.

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Web3 Data Lifecycle

Transfer Data On Chain

Launch Data to the Web3 Ecosystem

Integrated with decentralized storage such as IPFS, Arweave, and cloud storage such as AWS, Azure, and GCP. Users can customize the storage method.

Use Proxy Re-encryption or Homomorphic Encryption to encrypt stored data. Realize data transactions and calculations either remains undisclosed or not in their original form.

Store data Hash on chain and provide consistency verification service.

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Reliable Data Transaction

Use suitable attributes as verifiable credentials to differentiate the Data NFT’s transaction method.

Combined with Proxy Re-encryption, to realize different transaction types such as data access authorization, data security sharing, data ownership transfer, etc.

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Data Oracle

Feed off-chain data for smart contracts

Developers can use secure oracles to directly call any off-chain data API in smart contracts, such as identity information verification, credit inquiry, qualified investor verification, risk control, precision marketing, etc.

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Secure Data Computation

Datum builds a decentralized off-chain privacy-preserving computing network with decentralized scheduling service and on-chain economic incentives.

Outsourcing computation, give third parties the ability to contribute computing resources to support complex data application but the data are not in original form (in techniques like Homomorphic Encryption).

Data Cooperation, give data users the ability to pull insights from multiple datasets which remain undisclosed, without exchange at all times (in techniques like Secure Multi-party Computation).

Machine Learning, make multiple data and features available for AI model development and testing (in techniques like Privacy AI or Federated Machine Learning or TEE).

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