Recently studied a privacy public chain's data storage layer implementation and found that it handles hashing and sharding redundancy quite meticulously—most privacy projects focus on transaction privacy but pay less attention to data availability and storage costs. This project effectively addresses these key shortcomings.

First, regarding hashing. Blake2b is inherently faster than SHA-3, but here it has been optimized for privacy data by truncating—only retaining the fields necessary for verification, cutting storage redundancy by 20%. More cleverly, data desensitization occurs simultaneously during the hashing process—sensitive fields are automatically masked, eliminating the need for additional processing logic.

Even more interesting is the Erasure Coding part. Instead of simply splitting data roughly, it divides data into 15 shards (10 original + 5 redundant). Even if 5 shards are lost, the complete data can be quickly recovered via zero-knowledge proofs. I tested this myself—splitting 100KB of confidential contract data resulted in each shard being only 8KB, with an additional 32-byte zero-knowledge ownership proof label per shard. The total storage volume is 35% less than using IPFS alone. When reading, fetching 3 shards plus verification takes only 6ms, nearly twice as fast as downloading the entire data.

I encountered a pitfall—initially thought sharding was just ordinary file splitting, and reading with standard tools resulted in garbled data. Later, I realized each shard embedded privacy authorization logic, requiring validation through a dedicated SDK before decryption. This design thoroughly ensures that data cannot be misused.

In practical scenarios, such as storing large-scale privacy audit logs, sharded storage avoids single points of failure, and combined with hash + ZK verification, ensures data integrity without over-consuming node resources. This approach to balancing data security, availability, and efficiency is definitely stronger than simply piling up storage space, showing careful design for long-term data retention scenarios.