Nillion, a “blind computing” platform powered by multi-party computation, is integrating with Ethereum’s scaling layer, Arbitrum. This partnership aims to reduce settlement costs for applications built on Nillion while bringing its advanced privacy solutions to Arbitrum, according to a joint press release from both networks.
“This integration will enhance the privacy-preserving capabilities of existing dApps and open up new possibilities for developers to create secure, efficient, and user-friendly applications on Ethereum,” said Nillion CEO Alex Page.
Despite raising $20 million from prominent crypto participants like HashKey and OP Crypto, Nillion isn’t solely focused on blockchain or crypto use cases. Instead, its founders envision decentralizing a broad range of data processing tasks, beyond just finance. Unlike traditional blockchains that require nodes to maintain a complete transaction record, Nillion’s nodes can store and process sensitive, high-value data without any node having access to the actual data.
The concept of “blind computation” refers to using privacy-enhancing technologies such as multi-party computation and fully homomorphic encryption. This method allows blockchain applications to scale while preserving privacy by enabling the delegation of computation tasks without exposing input data.
Nina Rong, ecosystem lead at the Arbitrum Foundation, noted, “By combining their advanced privacy features with Arbitrum’s scaling solutions, we’re creating a powerful foundation for a new generation of decentralized applications.”
The integration will allow Nillion-based applications to settle on Arbitrum, leveraging the Layer 2’s liquidity. It also opens new design possibilities for Arbitrum’s dApps to access private storage and computation. In essence, Nillion functions as an oracle for blockchains.
Founded in 2021 by former executives from Uber, Indiegogo, Hedera Hashgraph, Coinbase, and Nike, Nillion recently partnered with decentralized AI startup Ritual to develop “blind AI inference technology,” aiming to democratize access to AI.
