Advanced Innovation Center for Future Blockchain and Privacy Computing

Index

• 区块链链下扩容技术方案
  • 概述
  • 主要流程
  • 链下交易过程
  • 链下共识过程
  • 链上通道智能合约
• 智能合约代码生成模型(CodeBC)
  • 1. 项目背景
  • 2. 模型架构
  • 3. 接口设计
  • 4. 数据准备
  • 5. 案例分析
• 基于 Lora 微调的大模型联邦分割框架
  • 1. 项目背景
  • 2. 模型架构
  • 3. 接口设计
  • 4. 数据准备
  • 5. 案例分析
• 基于强化学习的自适应聚合方法
  • 1. 项目背景
  • 2. 模型架构
  • 3. 接口设计
  • 4. 数据准备
• MetaVerse: 去中心化元宇宙原型系统
  • 1. 项目背景
  • 2. 项目架构
  • 3. 链上操作
  • 4. 后端用户加密接口
  • 5. 效果展示
  • 6. 结论
• 区块链密文检索技术方案
  • 概述
  • 密文检索方案
  • 接口介绍
  • 应用示例
• 区块链身份分层加密技术方案
  • 需求
  • 身份分层加密
  • 接口介绍

Overview

The center focuses on three core research directions: Blockchain, Privacy Computing, and Trusted Intelligence. These areas aim to provide foundational solutions for secure, private, and intelligent systems across various industries.

Research Directions

1. Blockchain: - Objective: Develop innovative blockchain technologies that enhance security, scalability, and decentralization for distributed applications. - Key Areas:

   • Blockchain consensus mechanisms

   • Smart Contract Code Generation Model

   • Off-chain scaling technology

   • Interoperability between different blockchain networks

   • Blockchain for supply chain management

   • Blockchain-enabled digital identity solutions

2. Privacy Computing: - Objective: Research and develop privacy-preserving technologies that protect user data while ensuring the functionality of digital systems. - Key Areas:

   • Homomorphic encryption

   • Zero-knowledge proofs

   • Privacy-preserving machine learning

   • Secure multi-party computation (SMPC)

   • Differential privacy

3. Trusted Intelligence: - Objective: Investigate intelligent systems that can provide trusted and reliable decision-making processes, incorporating AI, data security, and privacy. - Key Areas:

   • Explainable AI (XAI)

   • Secure AI model training and inference

   • Trusted hardware for AI and blockchain applications

   • Federated learning with privacy-preserving techniques

   • Integrity verification of AI decisions

Technological Integration

The three research directions will be integrated into a unified technological framework that: - Supports blockchain-enabled privacy to protect users in decentralized networks. - Applies AI techniques to blockchain and privacy systems to enhance decision-making and security. - Establishes trusted execution environments (TEE) and trusted hardware to ensure secure computation in both AI and blockchain applications.

Applications

• Blockchain + Privacy Computing: - Secure digital currency systems - Decentralized finance (DeFi) applications with enhanced privacy - Blockchain-based identity management systems

• Privacy Computing + Trusted Intelligence: - Secure and private AI-driven healthcare systems - Privacy-preserving autonomous systems - Trusted cloud computing environments

• Blockchain + Trusted Intelligence: - Blockchain-enabled secure AI model sharing and validation - Distributed ledger for verifiable AI decisions in critical applications (e.g., healthcare, autonomous driving)

Research Methodology

• Interdisciplinary Collaboration: The research center promotes collaboration between experts in blockchain, AI, and privacy technologies to develop solutions that integrate these fields seamlessly.

• Advanced Tools and Frameworks: Utilizing cutting-edge technologies for simulation and modeling of privacy-preserving algorithms, blockchain interactions, and intelligent systems.

Future Directions

The center aims to expand its focus on next-generation cryptographic protocols, quantum-resistant blockchain systems, and AI explainability in privacy-sensitive environments to stay at the forefront of technological innovation.