NEAR Public Chain Integrates Privacy Protocol Nillion: A Perfect Combination of Performance and Privacy

Recently, a well-known public blockchain has partnered with the privacy protocol Nillion to introduce blind computation and blind storage technologies into this high-performance L1 public blockchain. This integration combines the exceptional performance of the public blockchain with Nillion's advanced privacy tools, providing blind computation capabilities for hundreds of projects within the ecosystem.

This collaboration brings a modular data privacy solution to the public chain. Developers can flexibly perform data storage and computation operations within the Nillion network while achieving transparent settlement on the blockchain. This modular design provides greater flexibility for application architecture.

Nillion's private data management features significantly expand the capabilities of this public blockchain. By providing private storage and computation for various types of data, it greatly broadens the design space for privacy-preserving applications. Developers can now build solutions that were previously constrained by privacy limitations, attracting more privacy-conscious users.

In the field of artificial intelligence, this integration has also brought new possibilities. The public chain's focus on autonomous, user-owned AI complements Nillion's private storage and computing capabilities, opening up vast design space for decentralized AI.

This collaboration opens new avenues for privacy protection applications within the public chain ecosystem, especially in the area of AI solutions:

1. Private Inference: Nillion can achieve secure inference for AI models, protecting proprietary machine learning models and users' sensitive inputs.

2. Private Proxy: With the rise of AI proxies, Nillion's privacy solution has become particularly important, as it can protect users' privacy while using proxies.

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

4. Private Synthetic Data: Nillion can protect the privacy of the underlying data during GAN training.

5. Private Retrieval-Augmented Generation (RAG): Nillion provides an innovative privacy-preserving method for information retrieval.

In addition to the AI field, this integration has also brought new possibilities for other application scenarios:

• Cross-chain privacy solutions: Paving the way for privacy-preserving cross-chain applications and asset transfers.

• Privacy-first community platform: Decentralized communities can leverage private storage content and social graphs to achieve personalized recommendations.

• Secure DeFi: Enhance the security and privacy of the DeFi ecosystem through private order books, confidential loan assessments, and hidden liquidity pools.

• Privacy-focused developer tools: Providing privacy-centric tools and APIs that allow developers to easily integrate advanced privacy features into their applications.

This collaboration combines high-performance infrastructure with advanced privacy features, creating an ideal environment for developers to build powerful, privacy-preserving applications that meet the needs of the real world. This will help create a new open digital economy, allowing people to better control their assets and data.