What Technologies Does DAVE Have?
Not a person, but a big data solution!
- Trusted database
Solve the problem that traditional decentralized databases — the inability to provide reliable data storage in multi-party cooperation scenarios, and make up for the lack of decentralized applications in the database infrastructure at this stage.
Any data operation is transparent and cannot be tampered with, and the nodes can be truly equal to each other, which prevents the master node from performing malicious operations under the centralized architecture.
- Multi-mode read and write
There are three different write modes for database write operations to optimize database write performance. After dividing the two different data types, the data can be written to the blockchain in different ways for different data types, which reasonably optimizes the classification of public data and private data operations, and ensures that the database operations are legal and compliant. Facilitate enterprise customization.
- Data Recovery
The system needs to provide an interface to restore the data in the local database to make it consistent with the data in the blockchain, and continue the consensus after the service is restored. Ensure data consistency and integrity, and will not cause huge economic losses caused by malicious or unintentional deletion of the database.
- Blockchain information access
It mainly provides several query interfaces for blockchain so that users can obtain real-time information on the chain, such as blockchain block information, consensus-related information, blockchain status information, member information and other common query requirements. Ensure that information is timely, true and effective, and enable database supervision.
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Federated learning is to achieve common modeling on the basis of ensuring data privacy and security and legal compliance. A distributed machine learning technology enables two or more data-using entities to use the data in cooperation without data leaving local drive, solves the problem of data silos while maintaining privacy and ownership.
For federated learning, the working node represents the data owner of the model training. It has full autonomy over the local data and can independently decide when to join the federated learning for modeling. Relatively in the parameter server, the central node always occupy a dominant position, so federated learning thrives in a more complex learning environment.