The Perfect Fusion of Privacy and Performance: NEAR Public Chain Introduces Nillion Privacy Protocol

Recently, a certain privacy protocol announced the introduction of blind computing and blind storage technologies into the speed and scalability-focused L1 public chain NEAR. This innovative integration combines NEAR's high performance with advanced privacy tools, providing opportunities for over 750 projects in the NEAR ecosystem to utilize blind computing.

The Powerful Alliance Between NEAR and Nillion

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

1. Nightshade Sharding Technology: This is NEAR's unique sharding solution that significantly enhances transaction processing capacity and reduces latency, making it ideal for high-performance application scenarios.

2. WebAssembly Runtime Environment: NEAR's Wasm-based virtual machine supports smart contracts written in Rust and AssemblyScript, attracting developers from different technical backgrounds to participate.

3. User-friendly account system: NEAR adopts an intuitive and easy-to-understand account naming method, greatly improving user experience and accessibility.

These unique advantages have attracted a large number of developers, entrepreneurs, and innovators, collectively creating a thriving ecosystem with over 750 applications.

This time, the combination of blind computing power and NEAR's efficient transaction processing has achieved the following breakthroughs:

• Modular Data Privacy: Privacy features seamlessly integrate with NEAR, allowing for modular execution of data storage and computation operations in a dedicated network while achieving transparent settlement on the NEAR blockchain. This modular design provides developers with greater flexibility.

• Private Data Management: By providing private storage and computing services for various types of data, the functional boundaries of NEAR have been significantly expanded. This greatly extends the design space for privacy-protecting applications within the NEAR ecosystem, enabling developers to build solutions that were previously unattainable due to privacy constraints, while attracting more privacy-conscious users.

• Private AI: NEAR's emphasis on autonomous, user-driven AI complements its private storage and computing capabilities, opening up vast new horizons for the development of decentralized AI.

Expand the Construction Space for Cryptocurrency Projects

This technological integration has opened up new development directions for privacy protection applications within the NEAR ecosystem, especially in terms of AI solutions:

Private AI

• Private Inference: Supports secure inference on AI models, providing protection for proprietary machine learning models and users providing sensitive input, initially focusing on private models such as regression, time series forecasting, or classification.

• Private Agents: As the trend of AI agents operating in semi-autonomous or fully autonomous ways rises, the demand for privacy solutions becomes increasingly important. Support for intent classification can ensure that users do not disclose relevant information about the original query or the actions taken by the agent based on the query while using the agent.

• Federated Learning: Although federated learning primarily focuses on training models on decentralized datasets without centralizing data, new privacy technologies can enhance privacy protection by safeguarding the aggregation process, ensuring that sensitive information (such as gradients) generated during training remains confidential.

• Private synthetic data: New techniques can serve as solutions for protecting the privacy of underlying data during GAN training. Applying MPC to GAN training can ensure that the data used during training is not exposed to other participants.

• Private Retrieval-Augmented Generation (RAG): This technology introduces an innovative privacy-preserving method for information retrieval, supporting quantum-safe storage of vectors in a static state and semantic search evaluation without the need for decryption.

cross-chain privacy solution

Given NEAR's emphasis on interoperability, this integration is expected to 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, processing them to recommend targeted personalized content, combining the advantages of decentralization with privacy protection. Such platforms can also support anonymous voting, private proposal submissions, and secure fund management.

Safe DeFi

Blind computation technology enables private order books, confidential loan assessments, and hidden liquidity pools, greatly enhancing the security and privacy of NEAR's continuously growing DeFi ecosystem.

Privacy-focused Developer Tools

Blind computation technology can enhance the developer-friendly environment of NEAR by providing privacy-centric tools and APIs, enabling developers to easily integrate advanced privacy features into their applications while maintaining the ease of use and scalability of NEAR.

Outlook: The Future of Blind Computing on NEAR

By combining NEAR's high-performance infrastructure with advanced privacy features, an ideal environment is being created for developers to build powerful, privacy-preserving applications that meet real-world needs. This will help cultivate a whole new open digital economy, allowing users to truly take control of their assets and data.