NEAR blockchain integrates Nillion privacy protocol to usher in a new era of Web3 privacy

The NEAR public chain introduces the privacy protocol Nillion.

Recently, the privacy protocol Nillion announced a partnership with the NEAR public chain, introducing blind computation and blind storage technologies to this high-performance L1 public chain. This integration combines NEAR's efficient performance with Nillion's advanced privacy tools, enabling over 750 projects in the NEAR ecosystem to utilize blind computation technology.

NEAR, as a well-known L1 blockchain network in the industry, has always been renowned for its outstanding performance. Its main features include Nightshade sharding technology, a WebAssembly-based smart contract runtime environment, and a user-friendly readable account system. These characteristics have attracted a large number of developers and innovators, collaboratively building a thriving ecosystem.

By integrating Nillion's blind computation capabilities with NEAR's efficient transaction processing, breakthroughs have been achieved in the following areas:

1. Modular Data Privacy: Nillion's privacy features seamlessly integrate with NEAR, allowing for modular data storage and computation operations within the Nillion network while enabling transparent settlements on the NEAR blockchain.

2. Private Data Management: Nillion provides private storage and computing services for various types of data, greatly expanding the design space for privacy protection applications within the NEAR ecosystem.

3. Private AI: NEAR's focus on autonomous, user-owned AI complements Nillion's private storage and computing capabilities, opening up vast new design space for decentralized AI.

This integration opens up new avenues for privacy protection applications within the NEAR ecosystem, especially in terms of AI solutions:

• Private Inference: Nillion enables secure inference for AI models, protecting proprietary machine learning models and user sensitive inputs.

• Private Proxy: With the rise of AI agents, Nillion's privacy solutions have become crucial to protect the privacy of user queries and agent actions.

• Federated Learning: Nillion can enhance privacy by protecting the aggregation process, ensuring that sensitive information derived during training remains confidential.

• Private synthetic data: Nillion can become a solution for protecting the privacy of underlying data during GAN training.

• Private Retrieval-Augmented Generation (RAG): Nillion provides a new privacy-preserving method for information retrieval, achieving quantum-safe storage of vectors and semantic search evaluation.

In addition to the AI field, this integration has brought new possibilities to various areas such as cross-chain privacy solutions, privacy-first community platforms, secure DeFi, and developer tools that protect privacy.

By combining NEAR's high-performance infrastructure with Nillion's advanced privacy features, an environment is being created that allows developers to build powerful, privacy-preserving applications to meet real-world needs. This will help to create a new open digital economy that enables users to have better control over their assets and data.