Protocol Layer

The Protocol Layer is the core component of the ZenithRing system, responsible for ensuring data privacy, security, smart contract execution, and AI model analysis. This layer integrates blockchain, artificial intelligence, and Fully Homomorphic Encryption (FHE) technologies, ensuring the decentralization, security, privacy protection, and efficient intelligent analysis of the entire health data management system.

Blockchain and Data Storage

The Zenith Network distributed network primarily handles data storage and management. All health data is encrypted before being uploaded to the network, ensuring data privacy and security.

• Encrypted Storage and Tamper-Proofing: All health data uploaded to the blockchain is stored in encrypted form, ensuring transparency and immutability. The SHA-256 hashing algorithm is used to encrypt the data, ensuring that it cannot be accessed or tampered with by unauthorized parties during storage and transmission. Through blockchain's distributed ledger technology, ZenithRing ensures decentralized data storage, eliminating single points of failure and ensuring long-term data security and reliability.

• Data Ownership: All user health data is authenticated through smart contracts. Each time data is uploaded to the Zenith Network, the smart contract automatically executes and records the user's data ownership. This data is converted into tradable NFT assets and sent to the user's DID (Decentralized Identity) account, allowing users to freely control, authorize, and trade their health data while protecting their privacy. Users can receive rewards or incentives based on data usage and contribution levels and participate in ecosystem governance.

• Data Ownership and Trading: Through smart contracts, ZenithRing allows users to freely control their health data. Each piece of user health data uploaded to the Zenith Network is assigned a unique NFT identifier. Users can choose to share their data with research institutions, medical service providers, or trade it on the market, earning $ZEN tokens as incentives. All transactions are automatically executed through smart contracts, ensuring transparency and fairness.

Data Privacy Protection (Fully Homomorphic Encryption - FHE)

At the Protocol Layer, Fully Homomorphic Encryption (FHE) is the core technology for ensuring user data privacy. FHE allows computations to be performed on encrypted data without decrypting it, significantly enhancing data security and privacy protection.

• Data Encryption and Computation: During upload, storage, and processing, user health data remains encrypted. Only authorized computing nodes can process the data. FHE ensures that even when data is used for AI model training or provided to third parties, no personal privacy information is exposed, addressing common data leakage and privacy violation issues in cloud computing and data sharing.

• Privacy-Preserving Data Analysis: The application of FHE technology allows ZenithRing to perform health data analysis without decrypting the data. This enables the system to fully utilize health data for generating personalized health management recommendations while protecting user privacy. Users can safely participate in the decentralized health data market without worrying about personal privacy breaches.

AI Neural Network

The Protocol Layer of ZenithRing also includes a decentralized AI neural network for analyzing and processing uploaded health data, providing personalized health management recommendations. The AI neural network is not only used for real-time data analysis but also continuously learns from global user data, optimizing health management algorithms to ensure the accuracy and timeliness of health recommendations.

• AI Model Training: By integrating NPU (Neural Processing Unit) and a decentralized AI neural network, ZenithRing can perform multi-dimensional analysis of health data, including physiological data, exercise records, and nutritional information. This data is used to train AI models, generating personalized health plans for each user. The AI model can dynamically adjust health recommendations based on real-time user data, such as exercise goals, nutritional advice, and sleep improvement measures, helping users optimize their health management in real-time.

• Decentralized AI Training: Through Federated Learning and decentralized AI training, ZenithRing AI model can collect and analyze health data from users worldwide without centralizing all data on a single server. This approach ensures data privacy while leveraging extensive health data to optimize the model, improving the accuracy of personalized health recommendations.

• Intelligent Health Recommendation Generation: The AI neural network can not only process basic health data but also analyze more complex physiological signals, such as heart rate variability, exercise intensity, and sleep cycles. Based on this data, the system can provide in-depth health insights and generate dynamically adjusted health recommendations, helping users make more scientific health decisions in their daily lives.