NEAR Introduces Privacy Protocol: Enhancing Performance and Privacy Protection

Recently, a new privacy protocol announced the introduction of blind computing and blind storage technologies into the speed and scalability-focused public chain NEAR. This integration combines NEAR's high performance with advanced privacy tools, enabling many projects within the NEAR ecosystem to utilize blind computing technology.

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

1. Nightshade Sharding Technology: This is NEAR's unique sharding solution that significantly improves transaction throughput and reduces latency, making it very suitable for high-performance applications.

2. WebAssembly Runtime: NEAR's Wasm-based virtual machine supports smart contract development in Rust and AssemblyScript, attracting developers from diverse backgrounds.

3. Readable Accounts: NEAR uses an intuitive account name system, greatly improving user experience and accessibility.

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

By combining blind computing power with NEAR's efficient transaction processing, the following advantages have been achieved:

• Modular Data Privacy: Privacy features are 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. This modularity offers developers greater flexibility when designing application architectures.

• Private Data Management: By providing private storage and computation for all types of data, NEAR's capabilities have been expanded. This greatly broadens the design space for privacy-preserving applications within the NEAR ecosystem, allowing developers to build solutions that were previously impossible due to privacy constraints, and attract privacy-conscious users.

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

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

1. Private Inference: Implement secure inference for AI models, providing protection for proprietary machine learning models and users supplying sensitive inputs to them.

2. Private Agents: With the rise of AI agents acting autonomously, privacy solutions have become crucial. Intent classification support can ensure that users do not leak information about their original queries or agent actions while using the agents.

3. Federated Learning: Enhancing privacy by protecting the aggregation process, ensuring that sensitive information derived during training (such as gradients) remains confidential.

4. Private synthetic data: a solution to protect the privacy of base data during GAN training.

5. Private Retrieval-Augmented Generation (RAG): Enable novel privacy-preserving methods for information retrieval, facilitating the secure storage of vectors at rest and the evaluation of semantic search.

In addition, this integration has brought new possibilities to areas such as cross-chain privacy solutions, privacy-first community platforms, secure DeFi, and privacy-protecting developer tools.

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 to meet real-world needs. This will help to create a new open digital economy, allowing people to have better control over their assets and data.