NEAR public chain introduces privacy computing technology, perfectly integrating performance and privacy

Recently, the NEAR public chain announced the introduction of privacy computing technology, incorporating blind computation and blind storage into its high-performance L1 layer network. This innovative integration combines NEAR's efficient performance with advanced privacy protection tools, providing blind computing capabilities for over 750 projects within the ecosystem.

As a mature L1 blockchain network, NEAR is known for its outstanding performance. Its three core features include:

1. Nightshade Sharding Technology: Increases transaction throughput, reduces latency, suitable for high-performance applications.
2. WebAssembly Runtime: Smart contracts that support Rust and AssemblyScript, attracting developers from diverse backgrounds.
3. Readable Account System: Use intuitive account names to enhance user experience and accessibility.

These features have attracted a large number of developers, entrepreneurs, and creators, who have come together to build a thriving ecosystem.

This integration combines blind computation capabilities with NEAR's efficient transaction processing, achieving the following points:

• Modular Data Privacy: Privacy features seamlessly integrated with NEAR, allowing for modular execution of data storage and computation operations within a privacy network, while enabling 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 protection applications.
• Private AI: Complementing NEAR's focus on autonomous, user-owned AI, opening up 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:

1. Private AI:

   • Private inference: Achieve secure inference on AI models, protecting proprietary machine learning models and sensitive inputs.
   • Private agents: With the rise of AI agents, privacy solutions have become crucial.
   • Federated Learning: Enhances privacy by protecting the aggregation process, ensuring that sensitive information remains confidential during training.
   • Private synthetic data: Protect the privacy of base data during GAN training.
   • Private Retrieval Enhanced Generation: A novel privacy-preserving approach 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 while providing personalized recommendations.

4. Secure DeFi: Implement private order books, confidential loan assessments, and hidden liquidity pools.

5. Privacy-focused developer tools: Provide privacy-centric tools and APIs to facilitate developers in integrating privacy features.

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