Exploring the Future of Web3 Social: New Approaches to Address User Identification Issues

In the Web3 social domain, how to solve the user identification problem has always been an important challenge. This article will explore two emerging solutions: biometric methods and social guarantee methods.

The User Identification Dilemma of Social Media

Modern social media faces the problem of a proliferation of bots. Although platforms have a responsibility to uphold freedom of speech, the situation becomes complicated when "users" are actually bots. Bots have been shown to have a significant impact on public discourse, from interfering in elections to influencing public perceptions of major events.

Decentralized social platforms, while emphasizing anonymity and privacy, also inherit the "bot problem". In an era where artificial intelligence is becoming increasingly advanced, convincing users that the accounts on the platform are real and not bots has become a tricky issue.

It is clearly not feasible to simply adopt traditional KYC solutions, as this raises privacy issues. Why should users trust the platform to store sensitive personal data?

Therefore, the essence of the "user identification" issue is to seek a balance between confirming that the user "is indeed a person" and protecting personal data privacy. The following will explore two different solutions.

Biometric Authentication Solution

In the field of "personality proof", a well-known project has adopted a direct biometric solution: using retinal scans to create biometric proof, certifying that the user is a human and not a robot, and generating a certification token. The project claims to use zero-knowledge proofs to ensure the secure storage of biometric data.

The plan suggests that as AI plays an increasingly significant role in society, it is necessary to distinguish between humans and robots in a way that protects privacy and decentralization. Through retinal scanning, users can obtain an ID similar to a "digital passport," which may become the foundational mechanism for future digital social networks.

The project emphasizes its privacy protection measures, such as only storing iris hashes and using zero-knowledge proofs to share identification information. However, there are still many controversies, such as credentials being stolen, IDs being traded on the black market, and exploitation of users in developing countries.

In addition, the use of dedicated hardware for biometric identification has raised broader concerns. Even if the software is flawless, there is no guarantee that the hardware does not have backdoors that allow for the secret collection of users' actual biometric data.

Social Guarantee Methods

Another approach is to use social guarantees. The basic principle is that if multiple verified human users can "vouch" for someone, then that person is likely to be human as well. The key lies in designing a reasonable incentive mechanism to maximize the verification of human identification.

A typical project requires users to submit personal information and a deposit, certified by individuals already in the system, and through a challenge period. If there are objections, the case will be submitted to a decentralized court for judgment.

Other projects have also adopted similar social verification methods, such as video call verification and continuous verification code games. These social-based solutions seem less invasive compared to biometric solutions, and some even retain a certain degree of anonymity.

The Future of Human Identification

With the continuous advancement of AI, designing innovative human identification mechanisms has become increasingly important, not only concerning various incentive measures but also for better purifying and regulating future social networks.

However, from data privacy to the intrusiveness of processes, and to the effectiveness of verification, this process involves many trade-offs. Currently, there seems to be no perfect solution; one possible path is to adopt biometric methods in the short term and transition to social graph-based methods in the long term.

Looking to the future, this field needs more transparency in processes, code, and data. Only in this way can we truly create a social network infrastructure that aligns with the vision of decentralization and privacy protection.