Face Data Minting NFT: Privasea's Innovative Exploration

Recently, a facial recognition NFT minting project has attracted widespread attention. The project allows users to input their faces through a mobile application and mint them as NFTs. Since its launch at the end of April, it has attracted over 200,000 NFT mintings, demonstrating extremely high popularity.

This project is not just about simply converting facial data into NFTs; its core purpose is to verify the authenticity of users through facial recognition. In the current internet environment, bots account for a large portion of traffic, with malicious traffic making up 27.5%. This situation has caused significant trouble for service providers and ordinary users alike.

In the Web3 field, human-machine recognition is equally crucial. For example, in project airdrops, it is necessary to prevent cheaters from creating multiple fake accounts to launch attacks. For some high-risk operations, such as account login and withdrawal, it is essential to verify that the user is not only a real person but also the actual owner of the account.

Privasea has proposed an innovative solution: the Privasea AI Network built on FHE (Fully Homomorphic Encryption) to address the privacy computing issues in AI scenarios within the Web3 environment. The network consists of four roles: data owners, Privanetix nodes, decoders, and result receivers. Through a layered structure and optimized encapsulation, Privasea provides an efficient privacy computing solution.

The workflow of the Privasea AI Network includes steps such as user registration, task submission, task allocation, encrypted computation, key switching, result verification, incentive mechanisms, result retrieval, and result delivery. The entire process ensures the privacy of data and the integrity of computation.

To manage network nodes and allocate rewards, Privasea has launched WorkHeart NFT and StarFuel NFT, based on PoW and PoS mechanisms respectively. This combination of dual mechanisms optimizes the revenue distribution structure and balances the importance of computing resources and economic resources in the network.

FHE, as the core technology of the Privasea AI Network, is regarded as a new cryptographic holy grail. Compared to zero-knowledge proof (ZKP), FHE focuses more on privacy computing, while ZKP is mainly used for privacy verification. However, FHE also faces the challenge of slow computation speed. Nevertheless, with the development of technologies such as algorithm optimization and hardware acceleration, the performance of FHE is continually improving.

Privasea opens up new possibilities for the deep integration of Web3 and AI through its unique architecture and privacy computing technology. Although FHE still has room for improvement in computational speed, Privasea has partnered with ZAMA to tackle the challenges of privacy computing together. With continuous technological breakthroughs, Privasea is expected to unleash its potential in more fields and become a pioneer in privacy computing and AI applications.