The Combination of Privacy Computing and AI: Analyzing Privasea's Innovative Solutions

Recently, a facial recognition NFT minting project has attracted widespread attention. This project allows users to input their own facial data through a mobile application and mint it as an NFT. This seemingly simple concept has garnered over 200,000 NFT mints in a short period, demonstrating astonishing popularity.

The core purpose of this project is not merely to turn facial data into NFTs, but to verify users' real identities through facial recognition. This feature is of significant importance in the Web3 ecosystem, especially in preventing witch attacks and protecting high-risk operations.

However, implementing facial recognition technology in a Web3 environment faces numerous challenges. How to build a decentralized machine learning computing network? How to protect user data privacy? How to maintain network operation? These are all key issues that need to be addressed.

Privasea has proposed an innovative solution: the construction of the Privasea AI Network based on Fully Homomorphic Encryption (FHE) technology. This network optimizes FHE technology through a hierarchical structure, making it more suitable for machine learning scenarios.

The architecture of the Privasea AI Network includes four main roles: data owner, Privanetix node, decrypter, and result receiver. Its workflow covers the entire process from user registration to result delivery, ensuring the security of data and the privacy of computation.

The network employs a dual mechanism of PoW and PoS to manage nodes and allocate rewards. The introduction of WorkHeart NFT and StarFuel NFT provides users with flexible options for participating in network operations.

Although FHE technology performs excellently in privacy protection, it also faces challenges in computational efficiency. In recent years, various optimization schemes have emerged, including algorithm optimization and hardware acceleration, but the performance of FHE still has a significant gap compared to plaintext computation.

Privasea's solution opens up new possibilities for the integration of Web3 and AI. With continuous technological advancements, particularly in collaboration with ZAMA, Privasea is expected to achieve more breakthroughs in the fields of privacy computing and AI applications, providing users with a safer and more efficient data processing environment.