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

Recently, the NEAR public chain announced a collaboration with the privacy protocol Nillion to introduce blind computation and blind storage technologies into its ecosystem. This integration combines NEAR's high performance with Nillion's advanced privacy tools, enabling over 750 projects in the NEAR ecosystem to leverage blind computation technology.

NEAR, as a mature L1 blockchain network, has always been known for its outstanding performance. Its core features include:

1. Nightshade Sharding Technology: Increases transaction throughput and reduces latency, suitable for high-performance applications.
2. WebAssembly Runtime: Supports smart contract development in Rust and AssemblyScript, attracting developers from various fields.
3. Readable Account System: Uses intuitive account names to optimize user experience and accessibility.

Nillion's blind computation capability combined with NEAR's efficient transaction processing has achieved the following advantages:

• Modular Data Privacy: Allows for modular execution of data storage and computation operations within the Nillion network, while facilitating transparent settlement on the NEAR blockchain.
• Private data management: Provides private storage and computation for various types of data, expanding the design space for privacy-protecting applications within the NEAR ecosystem.
• Private AI: Resonating with NEAR's focus on autonomous, user-owned AI, it opens up new design spaces for decentralized AI.

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

1. Private AI:

   • Private Inference: Implement secure inference for AI models to protect proprietary machine learning models and sensitive inputs.
   • Private Proxy: Supports users in protecting the privacy of queries and actions while using AI proxies.
   • Federated Learning: Enhances privacy protection, ensuring that sensitive information remains confidential during the training process.
   • Private synthetic data: Protect the privacy of the underlying data during the GAN training process.
   • Private Retrieval-Augmented Generation (RAG): A privacy-preserving method for information retrieval.

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

3. Privacy-first community platform: Achieve private storage of content and social graphs, supporting blind voting, private proposal submissions, and secure fund management.

4. Secure DeFi: Implement private order books, confidential loan assessments, and hidden liquidity pools to enhance the security and privacy of the DeFi ecosystem.

5. Privacy-focused developer tools: Provide privacy-centric tools and APIs to help developers integrate advanced privacy features into applications.

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