NEAR Introduces Privacy Computing Technology, Balancing Performance and Privacy

Recently, the privacy protocol development team announced the introduction of blind computing and blind storage into a Layer 1 public blockchain that focuses on speed and scalability. This integration combines high-performance infrastructure with advanced privacy tools, enabling over 750 projects in the ecosystem to leverage blind computing technology.

This Layer 1 network is known for its performance, with key features including:

• Unique sharding solution: increases transaction throughput and reduces latency, suitable for high-performance applications.
• WebAssembly-based runtime: Supports smart contracts in Rust and AssemblyScript, attracting developers from diverse backgrounds.
• Readable Account System: Use intuitive account names to enhance user experience and accessibility.

These features have attracted numerous developers, entrepreneurs, and creators, who together have built a thriving ecosystem.

By combining blind computing power with efficient transaction processing, the following has been achieved:

• Modular Data Privacy: Privacy features seamlessly integrate with blockchain, allowing for modular execution of data storage and computation operations within privacy networks, while enabling transparent settlement on the blockchain.
• Private Data Management: Provides private storage and computation for all types of data, extending the capabilities of blockchain.
• Private AI: The focus of blockchain on autonomous, user-owned AI complements private storage and computing capabilities, opening up new design space for decentralized AI.

This integration opens new avenues for privacy protection applications, especially in AI solutions:

• Private Inference: Achieve secure inference on AI models to protect proprietary machine learning models and sensitive inputs.
• Private Proxies: With the rise of AI proxies, privacy solutions have become crucial.
• Federated Learning: Enhances privacy by protecting the aggregation process, ensuring that sensitive information derived during training remains confidential.
• Private synthetic data: Protecting the privacy of foundational data during GAN training.
• Private Retrieval-Augmented Generation (RAG): Enabling novel privacy-preserving methods for information retrieval.

In addition, this integration also opens up possibilities for cross-chain privacy solutions, privacy-first community platforms, secure DeFi, and privacy-preserving developer tools.

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