The Innovative Combination of Face Data and NFT: An In-depth Analysis of Privacy Computing Networks

Recently, a project that allows users to mint facial data into NFTs has sparked heated discussions. Since its launch at the end of April, over 200,000 NFTs have been minted, indicating its popularity. Behind this seemingly simple combination of facial data on the blockchain and NFTs lies profound technological innovation.

This article will delve into the project's purpose, technical principles, and the broader trend of the integration of Web3 and AI.

Continuous Adversarial Recognition between Humans and Machines

The core purpose of this project is not simply to mint facial data as NFTs, but to determine whether a user is a real person through facial recognition. This need arises from the ongoing human-machine confrontation issues in both Web2 and Web3 environments.

According to the data, malicious bots account for 27.5% of total internet traffic. These automated programs can have catastrophic consequences for services, severely impacting user experience. For example, in ticket grabbing, cheaters significantly increase their success rate through virtual accounts, leaving ordinary users with almost no chance.

In the Web2 environment, service providers distinguish between human and machine through real-name authentication, verification codes, and other methods. However, with the development of AI, traditional verification methods face challenges. In the Web3 environment, human-machine recognition is also a strong demand, especially in scenarios such as airdrops and high-risk operations.

However, implementing facial recognition in a decentralized Web3 environment involves deeper issues: how to build a decentralized machine learning computing network? How to protect user privacy? How to maintain network operation?

Innovative Exploration of Privacy Computing Networks

In response to the above issues, a certain team has built an innovative privacy computing network based on fully homomorphic encryption (FHE), aiming to solve the privacy computing problems in AI scenarios within Web3.

The core of this network is optimized FHE technology, designed to adapt to machine learning scenarios through a layered architecture consisting of an application layer, optimization layer, arithmetic layer, and primitive layer. This customized computing offers more than a thousand times the acceleration compared to basic solutions.

The network architecture includes four types of roles: data owners, computing nodes, decryptors, and result receivers. The workflow is roughly as follows:

1. Users register and submit encrypted computing tasks and data.
2. Smart contracts assign tasks to appropriate computing nodes
3. The node performs cryptographic computations and generates zero-knowledge proofs.
4. Ensure result security through key switching
5. The decryptor verifies the integrity of the computation and decrypts the result.
6. Deliver the results to the designated recipient

The network uses an open API, lowering the barrier for users. At the same time, end-to-end encryption protects data privacy. The network also combines PoW and PoS mechanisms for node management and reward distribution, balancing computational resources and economic resources.

Advantages and Limitations of FHE Technology

FHE, as the core technology of the network, has its own advantages and disadvantages compared to other schemes such as zero-knowledge proofs ( ZKP ). FHE focuses on privacy computing, while ZKP focuses on privacy verification. Compared to secure multiparty computation ( SMC ), FHE has advantages in certain scenarios.

FHE has achieved the separation of data processing rights and ownership, but it has also come at the cost of computational speed. In recent years, significant improvements in FHE performance have been made through methods such as algorithm optimization and hardware acceleration. However, there is still a considerable gap compared to plaintext computation.

Conclusion

This innovative attempt that combines facial data, NFT, and privacy computing opens up new pathways for the deep integration of Web3 and AI. Although the underlying technology still has limitations, with continuous breakthroughs, such solutions are expected to unleash potential in more fields and promote the development of privacy computing and AI applications.