Technical Architecture

Data Layer

Storage System: Utilizes decentralized storage systems like IPFS (InterPlanetary File System) to ensure data persistence and accessibility. IPFS offers efficient data retrieval and version control while reducing the risk of single points of failure.
Blockchain Technology: Leverages blockchain platforms such as Ethereum to store transaction records and smart contracts, ensuring transparency and immutability of data transactions.
Data Encryption: All stored data is encrypted using advanced encryption standards such as AES and RSA before entering the network to prevent unauthorized access.

Encryption Layer

Fully Homomorphic Encryption (FHE): Allows complex computations directly on encrypted data without decryption, crucial for preserving privacy during data usage.
Secure Multi-Party Computation (MPC): Enables multiple parties to collaboratively compute a function without revealing their inputs, used for privacy-preserving data analysis and AI training.

Network Layer

Network Optimization: Enhances data transmission efficiency and reduces bandwidth consumption through techniques like network coding and data compression.
Dynamic Routing Algorithms: Optimizes data transmission paths using dynamic routing algorithms to reduce latency and improve data transfer speeds across different geographical locations.

Computation Layer

Distributed Computing Framework: Implements automated deployment and elastic scaling of computational tasks based on containerization technologies like Docker and Kubernetes.
AI Model Training and Inference: Supports various machine learning frameworks and libraries such as TensorFlow and PyTorch to accommodate complex AI training and inference tasks.
Resource Scheduling Optimization: Utilizes advanced resource scheduling algorithms, including priority-based task scheduling, to ensure efficient utilization of computational resources.

Contract Layer

Smart Contracts: Automatically executes predefined protocols using smart contracts, such as handling data usage permissions and completing transaction settlements.
Contract Automation Testing: Implements continuous integration and automated testing processes to ensure the security and correctness of smart contracts.
Audit and Compliance: Integrates blockchain auditing tools to monitor and verify the execution of smart contracts in real-time, ensuring compliance with legal and regulatory requirements.

Application Layer

User Interface: Provides intuitive web and mobile application interfaces, enabling users to easily manage their data and execute AI models.
API Access: Offers RESTful APIs, allowing developers and enterprise customers to conveniently integrate AIET platform functionalities into their own applications.
Multi-Language Support: Supports SDKs for various programming languages and frameworks, expanding the platform's applicability and ease of use.

Optimization Layer

Performance Monitoring: Real-time monitoring of system performance metrics, such as response time and resource utilization, to promptly identify and address performance bottlenecks.
Algorithm Optimization: Iteratively optimizes encryption and data processing algorithms to improve efficiency and reduce computational costs.
User Experience Optimization: Continuously improves user interface and interaction flows based on user feedback and usage data, enhancing user satisfaction.

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Last updated 12 months ago

Data Layer

Storage System: Utilizes decentralized storage systems like IPFS (InterPlanetary File System) to ensure data persistence and accessibility. IPFS offers efficient data retrieval and version control while reducing the risk of single points of failure.

Blockchain Technology: Leverages blockchain platforms such as Ethereum to store transaction records and smart contracts, ensuring transparency and immutability of data transactions.

Data Encryption: All stored data is encrypted using advanced encryption standards such as AES and RSA before entering the network to prevent unauthorized access.

Encryption Layer

Fully Homomorphic Encryption (FHE): Allows complex computations directly on encrypted data without decryption, crucial for preserving privacy during data usage.

Secure Multi-Party Computation (MPC): Enables multiple parties to collaboratively compute a function without revealing their inputs, used for privacy-preserving data analysis and AI training.

Network Layer

Network Optimization: Enhances data transmission efficiency and reduces bandwidth consumption through techniques like network coding and data compression.

Dynamic Routing Algorithms: Optimizes data transmission paths using dynamic routing algorithms to reduce latency and improve data transfer speeds across different geographical locations.

Computation Layer

Distributed Computing Framework: Implements automated deployment and elastic scaling of computational tasks based on containerization technologies like Docker and Kubernetes.

AI Model Training and Inference: Supports various machine learning frameworks and libraries such as TensorFlow and PyTorch to accommodate complex AI training and inference tasks.

Resource Scheduling Optimization: Utilizes advanced resource scheduling algorithms, including priority-based task scheduling, to ensure efficient utilization of computational resources.

Contract Layer

Smart Contracts: Automatically executes predefined protocols using smart contracts, such as handling data usage permissions and completing transaction settlements.

Contract Automation Testing: Implements continuous integration and automated testing processes to ensure the security and correctness of smart contracts.

Audit and Compliance: Integrates blockchain auditing tools to monitor and verify the execution of smart contracts in real-time, ensuring compliance with legal and regulatory requirements.

Application Layer

User Interface: Provides intuitive web and mobile application interfaces, enabling users to easily manage their data and execute AI models.

API Access: Offers RESTful APIs, allowing developers and enterprise customers to conveniently integrate AIET platform functionalities into their own applications.

Multi-Language Support: Supports SDKs for various programming languages and frameworks, expanding the platform's applicability and ease of use.

Optimization Layer

Performance Monitoring: Real-time monitoring of system performance metrics, such as response time and resource utilization, to promptly identify and address performance bottlenecks.

Algorithm Optimization: Iteratively optimizes encryption and data processing algorithms to improve efficiency and reduce computational costs.

User Experience Optimization: Continuously improves user interface and interaction flows based on user feedback and usage data, enhancing user satisfaction.