Decentralized Storage (IPFS) and Efficiency Improvements

Decentralized storage is another key technology for DataSci, especially for securing and efficiently accessing data through IPFS (InterPlanetary File System), a distributed file storage protocol that ensures platform resiliency and high availability by slicing and dicing data and storing it across multiple nodes. However, the popularity of decentralized storage also brings some performance and efficiency challenges, especially in terms of data access speed and storage cost.

IPFS Node Collaboration and Load Balancing

• Decentralized Node Network: DataSci utilizes distributed computing nodes to increase the speed of storage and data transfer. Multiple global nodes will share data storage and access tasks, avoiding single points of failure and improving data availability and stability.

• Load Balancing Mechanism: To prevent the overloading of a single node, DataSci introduces a load balancing system that intelligently schedules user access requests and assigns them to less loaded nodes, thus improving access speed and ensuring high availability of the platform.

End-to-End Data Encryption

DataSci uses end-to-end encryption to secure data both in transit and at rest. When a user uploads data, it is immediately encrypted before being transmitted to the decentralized storage network. Only authorized users with the correct decryption key can access the data, ensuring that data remains confidential and secure during every stage of the process. This encryption method guarantees that even if a node is compromised, the data itself remains unreadable without the appropriate cryptographic key.

Storage Efficiency Optimization

• Content Addressing: The IPFS data storage mechanism relies on content addressing rather than traditional file locations. This mechanism ensures the uniqueness and addressability of each file through a hash value so that users do not need to care about the physical storage location of the file, but through the hash value to directly access the data. This significantly improves the efficiency of data storage and retrieval.

• Data Tiering and Caching: To reduce storage and access costs, DataSci introduces data tiering and caching techniques. For more frequently accessed data sets, the platform caches them on efficient nodes to ensure fast reads while reducing the burden on the primary storage network. Through this tiered storage mechanism, the platform can balance the access speed and storage cost of data and improve overall performance.