Facial Data NFTization: Exploring the Innovative Fusion of Web3 and AI

Recently, a facial recognition NFT minting project has attracted widespread attention. The project allows users to mint their facial data into NFTs through a mobile application, and since its launch, it has attracted over 200,000 users to participate. This phenomenon embodies deep technological innovation and exploration of application scenarios.

The Ongoing Challenge of Human-Machine Recognition

Machine recognition has always been a key issue in the internet world. According to data, in the first quarter of 2024, malicious bot traffic accounted for 27.5% of total internet traffic. These automated programs not only impact user experience but can also cause serious harm to service providers.

In the Web2 environment, various methods such as CAPTCHA and real-name authentication are used to distinguish between humans and machines. However, with the rapid development of AI technology, traditional verification methods face new challenges. Verification methods have to gradually upgrade from behavioral feature detection to biometric recognition.

The Web3 space also faces the demand for human-machine recognition, especially in preventing witch attacks and protecting high-risk operations. However, achieving effective facial recognition in a decentralized environment while protecting user privacy has become a complex technical challenge.

Innovative Attempts of Privacy Computing Networks

To address the challenges of AI applications in the Web3 environment, a company has built a privacy AI network based on fully homomorphic encryption (FHE) technology. This network optimizes encapsulation to adapt FHE technology to machine learning scenarios, providing a thousand times the computational acceleration compared to basic solutions.

This network includes four types of roles: data owners, computing nodes, decoders, and result receivers. Its core workflow covers the entire process from user registration, task submission to result verification, ensuring the privacy and security of data throughout the processing.

The network uses a dual mechanism of PoW and PoS to manage nodes and allocate rewards. Users can participate in network computing and earn profits by purchasing specific NFTs, and they can also increase their yield multiplier by staking tokens. This design leverages actual work output while balancing the distribution of economic resources.

Advantages and Limitations of FHE Technology

Fully Homomorphic Encryption (FHE), as an emerging cryptographic technology, shows great potential in the field of privacy computing. Compared to Zero-Knowledge Proof (ZKP) and Secure Multi-Party Computation (SMC), FHE is more suitable for complex computing scenarios that require data privacy protection.

However, FHE also faces challenges in computational efficiency. Despite some progress in algorithm optimization and hardware acceleration in recent years, the performance of FHE still lags significantly behind that of plaintext computation.

Future Outlook

With the continuous advancement of technology and the expansion of application scenarios, privacy computing networks based on FHE are expected to play a role in more fields. This attempt to deeply integrate Web3 with AI not only provides users with a secure data processing environment but also opens up new possibilities for future privacy-protecting AI applications.