NEAR introduces privacy computing technology to enhance ecological security and the potential for AI applications.

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NEAR Public Chain Introduces Privacy Computing Technology, Merging Performance and Privacy Implementation

Recently, the NEAR public chain announced the introduction of blind computation and blind storage technology. This important upgrade will bring new privacy protection capabilities to its ecosystem. By integrating advanced privacy tools with NEAR's high-performance infrastructure, over 750 projects in the ecosystem will be able to utilize blind computation features, providing users with a safer and more private experience.

NEAR, as a mature L1 blockchain network, has always been known for its outstanding performance. Its core features include Nightshade sharding technology, WebAssembly-based smart contract runtime, and an intuitive readable account system. These innovations have attracted a large number of developers and driven the prosperous development of the NEAR ecosystem.

NEAR Public Chain Introduces Privacy Nillion: The Intersection of Privacy and Performance

The introduction of this privacy technology will bring the following improvements to NEAR:

  1. Modular Data Privacy: Developers can flexibly handle data storage and computation within privacy networks while ensuring transparent settlement on the NEAR blockchain.

  2. Private Data Management: Expands the functionality of NEAR to provide private storage and computation services for various types of data.

  3. Private AI Support: Combining NEAR's focus on autonomous AI has opened up new design space for decentralized AI applications.

This episode paves new ways for privacy protection applications within the NEAR ecosystem, especially in the field of AI solutions:

  • Private Inference: Protect proprietary machine learning models and user sensitive inputs.
  • Private AI Agent: Ensures that users do not leak sensitive information while using the AI agent.
  • Federated Learning: Enhancing privacy protection in model training on decentralized datasets.
  • Private synthetic data: Protect the data privacy during the GAN training process.
  • Private Retrieval-Augmented Generation (RAG): A privacy-preserving information retrieval method.

In addition to the AI field, this technology will also promote the development of cross-chain privacy solutions, privacy-first community platforms, secure DeFi applications, and privacy protection developer tools.

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

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GhostChainLoyalistvip
· 07-08 07:30
Looking forward to seeing the truth in practice.
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GasFeeCryervip
· 07-08 07:20
The upgrade has stabilized this wave.
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StakeTillRetirevip
· 07-08 07:14
Privacy solutions worth copying homework
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