NEAR Public Chain Introduces Privacy Technology: A Perfect Combination of Performance and Confidentiality

Recently, a protocol focused on privacy computing announced the introduction of its blind computing and blind storage technologies to the NEAR public chain, known for its speed and scalability. This integration combines NEAR's high performance with advanced privacy protection tools, enabling numerous projects within the NEAR ecosystem to utilize blind computing technology.

The Integration of NEAR and Privacy Technology

NEAR, as a mature Layer 1 blockchain network, has always been known for its excellent performance. Its main features include:

1. Nightshade Sharding Technology: Enhances transaction processing capacity, reduces latency, suitable for high-performance applications.
2. WebAssembly Runtime: Supports smart contracts written in Rust and AssemblyScript, attracting developers from multiple fields.
3. User-friendly account system: Uses intuitive account names to optimize user experience.

These features have attracted a large number of developers, entrepreneurs, and innovators, who have together created a thriving ecosystem with hundreds of applications.

Combining blind computing capabilities with NEAR's efficient transaction processing achieves the following advantages:

• Modular Data Privacy: Privacy features seamlessly integrated with NEAR, allowing for modular data storage and computation execution in a privacy network while enabling transparent settlement on the NEAR blockchain.
• Privacy Data Management: Expanded NEAR's functionality by providing private storage and computation for various types of data.
• Privacy AI: NEAR's focus on autonomous, user-driven AI complements its capabilities in private storage and computation, opening up new design spaces for decentralized AI.

Expanding the Field of Cryptocurrency Project Development

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

Privacy AI

• Private Inference: Achieve secure inference on AI models, protecting proprietary machine learning models and user sensitive inputs.
• Private Proxy: With the rise of AI proxies, privacy solutions have become crucial.
• Federated Learning: Enhances privacy by protecting the aggregation process, ensuring that sensitive information remains confidential during the training process.
• Private Synthetic Data: Protect the privacy of the foundational data during GAN training.
• Private Retrieval-Augmented Generation (RAG): A novel privacy-preserving method for information retrieval.

Cross-chain Privacy Solution

Considering NEAR's emphasis on interoperability, this integration can pave the way for privacy-preserving cross-chain applications and asset transfers.

Privacy-First Community Platform

Decentralized communities can leverage privately stored content and social graphs to process and recommend personalized content, combining the advantages of decentralization with privacy protection.

Secure Decentralized Finance

Blind computing technology can enable private order books, confidential loan assessments, and hidden liquidity pools, enhancing the security and privacy of the continuously evolving decentralized finance ecosystem of NEAR.

Privacy-focused development tools

Enhance the developer-friendly environment of NEAR by providing privacy-focused tools and APIs, enabling developers to easily integrate advanced privacy features into their applications.

The Future Outlook of Blind Computation on NEAR

The combination of NEAR's high-performance infrastructure and advanced privacy features is creating an environment 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 take control of their assets and data.