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Nillion and NEAR Protocol Collaborate to Advance Decentralized AI Applications

source-logo  cryptonewsland.com 14 September 2024 00:14, UTC
  • Nillion partners with NEAR Protocol to advance blockchain data privacy and AI application development.
  • NEAR Protocol enhances its features with Nillion’s blind computing for improved dApp privacy and efficiency.
  • Nillion continues expansion, follows Arbitrum integration with strategic NEAR Protocol partnership for decentralized AI.

Nillion, a technology company that focuses on blind computing, has entered into a strategic partnership with the NEAR Protocol, a distributed ledger technology. This combination helps to advance the development of data protection and data management features for blockchain systems and place NEAR at the forefront of decentralized application creation with an emphasis on AI applications.

Nillion 🤝 @NEARProtocol

All 750+ projects in the NEAR ecosystem will have access to Blind Compute.

Unlocking a privacy-first design space for truly decentralized AI applications.

Check out the link in comments for more details pic.twitter.com/xAR0APvbfn

— Nillion (@nillionnetwork) September 13, 2024

Enhanced Privacy Tools for Developers

The collaboration between Nillion and NEAR Protocol is designed to offer developers access to sophisticated privacy tools that safeguard user information while optimizing data storage and computation processes. By employing Nillion’s unique technology, data can be fragmented and distributed across a network of nodes, effectively enhancing processing speeds and security. This technology is particularly beneficial for high-computation applications, including those that leverage AI, where privacy and speed are paramount.

NEAR Protocol, recognized for its innovative features like Nightshade sharding and a WebAssembly-based runtime for smart contracts, will significantly benefit from this integration. The partnership aims to improve NEAR’s existing attributes by facilitating modular execution of data storage and computation operations on the Nillion network. This integration promises a more efficient and secure environment for developers, paving the way for advanced decentralized applications (dApps).

Empowering Self-Sovereign Artificial Intelligence

This integration has several objectives one of which is to enable developers come up with AI solutions that are strong and at the same time friendly with the privacy of the users. Illia Polosukhin, founder of NEAR Protocol and a former contributor to the TensorFlow machine learning platform, sees the protocol as a home for self-sovereany AI. This vision aligns closely with Nillion’s privacy-first approach, which aims to offer a decentralized framework where users maintain control over their data.

The potential applications of this enhanced privacy framework are extensive. Developers can now explore new avenues in private AI applications, federated learning, and the generation of private synthetic data. The technology also holds promise for improving privacy in cross-chain applications and within the decentralized finance (DeFi) ecosystem, including private order books and confidential loan assessments.

Strategic Integration Continuation

This is not Nillion’s first foray into integrating with prominent blockchain networks. Recently, the platform also launched an integration with the Ethereum scaling layer Arbitrum, which marked a significant expansion in its network capabilities. These strategic integrations are part of Nillion’s broader aim to enhance blockchain functionality and scalability across various platforms.

The partnership with NEAR Protocol not only extends the practical utility of Nillion’s technology but also enhances NEAR’s capabilities, providing a robust infrastructure for developing privacy-preserving applications. This collaboration is backed by substantial support from the crypto community, with Nillion having secured significant funding from leading crypto firms, which underscores the industry’s confidence in their innovative solutions.

cryptonewsland.com