Human Rank: Progressive and Fair Approach to Identity Verification

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Introduction

The modern world faces the growing significance and complexity of identity verification. With the development of the internet and digital technologies, the need for reliable and effective methods of identity verification is becoming increasingly relevant. Traditional methods, such as passwords and personal data, have become vulnerable to cyber-attacks and fraud. Simultaneously, there is a growing need for unequivocal identity verification (and its uniqueness) beyond the frameworks of states, organizations, and territories.

I propose the “Human Rank” approach, which is a combination of existing identity verification methods and technologies: the concept of “verification by chain” with a ranking weight calculation mechanism similar to the Page Rank algorithm (Google), while combining it with self-identification principles that allow individuals to control their personal data and provide it as needed.

The key idea of “Human Rank” is to use connections between acquaintances to verify identity. If a person is verified by someone who has already passed verification, their trust level increases, and their rank rises.

This approach has several advantages. Firstly, it is decentralized and reliable due to the use of blockchain technology. Secondly, “Human Rank” does not rely on central databases and eliminates the need for centralized institutions for identity verification. This makes the identification process simpler, more cost-effective, and accessible to everyone. This approach reduces the risks of data leaks and increases trust in identity verification systems as a whole.

As far as I know, the term “Human Rank” has not been widely used or known as a specific term in the context of identity verification or other fields.

However, it is important to consider a critical view of this approach and evaluate its drawbacks. There are potential risks of rank manipulation and the creation of fake profiles and connections. It is also important to consider data protection and privacy aspects to prevent possible information leaks.

In this article, I will try to outline the details of applying the “Human Rank” method in real life, its economic benefits, and practical aspects of implementation. We will also discuss the role of the human factor in decision-making based on “Human Rank” and possible ways to motivate people to use this method. Ultimately, the article will provide a better understanding of the advantages and limitations of “Human Rank” and its role in modern identity verification systems.

Self-Identification and the Concept of Human Rank

What could be better and fairer than allowing a user, employee, or citizen to independently determine what data and to whom they are willing to provide access.

Self-identification is a process where individuals manage their personal data themselves and provide it as needed or desired. Unlike traditional methods where personal data control is held by centralized institutions, self-identification allows decentralized identity verification and data control.

Self-identification offers several advantages over traditional approaches. Firstly, it provides enhanced security and reliability, as personal data is not stored in centralized databases that can be compromised. Secondly, self-identification gives individuals full control over their data, allowing them to provide it only as necessary and with consent. This promotes privacy protection and prevents unauthorized data use. Additionally, the ability to revoke access to personal data or even delete it altogether offers a fair approach to the inalienable right of each person to fully manage their personal information.

The protection of an individual’s personal data is a fundamental human right related to privacy and confidentiality. These rights grant individuals control over their personal information and establish limitations on the collection, use, storage, and disclosure of such information to other persons or organizations. Key rights related to personal data protection include:

• Right to privacy: This right ensures that personal data remains confidential and cannot be used without consent or in accordance with the law.
• Right to information: Individuals have the right to know what data is being collected about them, for what purposes, and how it will be used.
• Right to access and rectification: Individuals have the right to request access to their personal data and correct inaccurate or outdated information.
• Right to erasure (“right to be forgotten”): This right allows individuals to request the deletion of their personal data, especially if it is no longer needed for the purposes for which it was collected or if the data processing was unlawful.
• Right to restrict processing: Individuals have the right to request the restriction of their data processing in certain circumstances, such as when the accuracy of the data is contested or the processing is unlawful.
• Right to data portability: This right allows individuals to receive their personal data in a structured, commonly used, and machine-readable format and transfer it to another organization.
• Right to object to automated decision-making: Individuals have the right not to be subject to decisions based solely on automated processing of their personal data that have legal or significant effects on them.

These rights are based on international and national data protection laws and allow individuals to manage their personal data and protect their privacy. Today, only self-identification can fully guarantee the observance of these rights.

Human Rank is an addition to the self-identification method, created by analogy with the Page Rank algorithm (PageRank (PR) is an algorithm used by Google Search to rank web pages in their search engine results. It is named after both the term “web page” and co-founder Larry Page. PageRank is a way of measuring the importance of website pages).

In this approach, in addition to self-identification, users verify each other’s identities, creating chains of trust. If someone’s identity is confirmed by someone who has already been verified, their rank increases, which raises their trust level.

In the Page Rank algorithm, a formula was used to calculate the rank of each specific page on the Internet based on the presence of links to it and the weight of each specific link calculated iteratively for all documents on the Internet.

As in Page Rank calculations, Human Rank calculations do not use any subjective assessments, only the presence of a connection between two objects. The further one object (subject in the case of Human Rank) is from another, the lower the weight transferred from one to another. For this, the Page Rank algorithm used a damping factor that imitated the decreasing probability of a user moving from one page to another. Similarly, Human Rank proposes using a damping factor that simulates the decreasing probability of acquaintance between two individuals depending on the number of acquaintances between them. For calculating this factor, the principle of “Six Degrees of Separation” was used, which suggests that one random person on the planet is connected to another through an average of seven acquaintances.

Self-identification and the Human Rank algorithm represent an interesting combination of methods for fair, reliable, and secure identity verification. They provide individuals with the ability to control their data and increase trust in identity verification systems. These innovative approaches open new horizons in personal information protection and create a solid foundation for decentralized identification systems.

It is important to understand that there is no universally ideal method of identity verification suitable for all situations. Depending on the context and specific requirements, the most appropriate and effective method can be chosen, and a combination of different methods can be used to ensure maximum reliability and security of identity verification.

Chain-Based Identity Verification

One innovative approach to ensuring reliable identity verification is the “chain-based verification” method, which relies on trust and connections between acquaintances.

The chain-based identity verification method is based on trust and accountability among acquaintances. In this approach, each individual who undergoes verification invites another individual into the system (a referral program). The verification is not conducted by a centralized institution but through connections between acquaintances.

When one individual invites another, they bear responsibility for that person. This means that if the invited individual successfully passes verification and demonstrates their social responsibility by inviting others and verifying them, the initial individual receives a reward. Thus, the chain-based identity verification method incentivizes individuals to act honestly and responsibly.

Chain-based identity verification offers several advantages. Firstly, it is based on trust among acquaintances, which enhances the credibility and reliability of verification. Secondly, this approach allows for the decentralization of the verification process, protecting personal data from potential compromise.

Moreover, the chain-based verification method promotes responsible behavior. Individuals who invite and verify others become socially responsible and receive rewards for their actions.

However, this method also has its limitations. Firstly, it requires active participation from individuals, which may be inconvenient for some users. Secondly, chain-based verification requires a certain number of connections among acquaintances, which can complicate the process for new users who do not have acquaintances within the system to invite them.

The previously mentioned Human Rank algorithm, analogous to Google’s algorithm, involves calculating the weight (rank) of each user in the system based on the number of their connections. This is intended to motivate participants to be more active in inviting others to the system while balancing the weight transferred to new participants through the damping factor introduced in the Human Rank algorithm.

The damping factor in chain-based verification determines the probability that a person can link their rank with another person’s rank through a chain of acquaintances, as in the “Six Degrees of Separation” theory. To calculate the damping factor, we can use the following formula:

Damping Factor (d) = 1/Number of acquaintances in the chain of acquaintances

If each person is connected to another through 7 degrees of separation, this means the number of acquaintances in the chain of acquaintances is 7. Thus, the damping factor would be:

Damping Factor (d) = 1/7 ≈ 0.1429

Therefore, in this example, the damping factor is approximately 0.1429 or about 14.29%. This factor determines the likelihood of connections between individuals through the chain of acquaintances in the chain-based verification method, allowing the structure of social connections to be considered when calculating Human Rank and accounting for the possibility of links between participants through the chain of acquaintances.

Human Rank in Real Life

The Human Rank method can be successfully applied in various areas for identity verification and trust enhancement. Here are several examples of potential successful implementation:

1. Social Networks: Social media platforms use Human Rank to verify the authenticity of users. Users can confirm each other’s identity through connections in their profiles, helping to create a trustworthy community.
2. Financial Institutions: Banks and financial companies use Human Rank to verify the identity of clients. The invitation into the system and the responsibility of the referrer provide additional reliability guarantees for the client.
3. Work and Business: Recruiters and employers can use Human Rank to verify professional recommendations and candidate experience. This helps to ascertain the authenticity of the information in resumes and make informed hiring decisions.
4. Fair Distribution of Benefits: Systems for distributing limited resources, such as charitable organizations or government aid programs, can use Human Rank to ensure fair and transparent distribution of benefits. Identity verification through the trust chain helps confirm the authenticity and needs of applicants.
5. Online Voting and Surveys: To prevent manipulation and ensure the integrity of results, platforms for online voting and surveys can use Human Rank to verify participants. This ensures that each vote or response belongs to a real, unique person.
6. Education and Training: Educational institutions can apply Human Rank to verify the authenticity of applications for courses, exams, or scholarships. This helps ensure that candidates truly meet the requirements and possess the necessary skills and knowledge.
7. Freelancer and Remote Work Platforms: Freelancer platforms can use Human Rank to verify the authenticity of profiles and reviews. This helps create a trustworthy community and protect users from fraud.

Critical Perspective

While the Human Rank method represents a promising approach to identity verification, it also has its critics and limitations.

Subjectivity of Ratings: Rankings based on social connections can be subjective and depend on the opinions of others. This can lead to distorted results and reduce the accuracy of identity verification.

Limited Social Connections: In some cases, a person may have a limited number of social connections, which can make it difficult to verify their identity through others.

Possibility of Manipulation: The Human Rank method can be subject to manipulation if participants in the system abuse their social connections to improve their rank.

Incomplete Representation: Some people may be part of different social networks or communities that do not intersect. This can lead to an incomplete representation of their identity.

Ways to Minimize Drawbacks:

To minimize the drawbacks and risks associated with the use of Human Rank, the following approaches can be applied:

Trust Consideration: When calculating Human Rank, the trustworthiness of rating sources and social connections should be considered. This will help reduce subjectivity and prevent possible manipulations.

Additional Verifications: To increase the reliability of identification, additional checks can be introduced, such as document verification or biometric methods.

Network Expansion: The system should encourage participants to expand their social connections and create trust networks to improve identification accuracy.

Control and Transparency: It is necessary to ensure the transparency of the Human Rank calculation process and monitor its implementation to prevent possible abuses.

A critical view of the Human Rank method reveals potential problems and drawbacks, but with the right approach and the application of risk minimization strategies, this method can become an effective and trusted means of identity verification. It offers significant advantages compared to traditional methods like KYC and may even replace them, which is currently being worked on by the startup Shegby — the Network State.

Conclusion

The Human Rank method, combined with the concept of self-identification, presents a novel and promising approach to identity verification. By leveraging social connections and decentralizing the process, it offers several advantages over traditional methods, such as enhanced security, privacy, and user control over personal data. However, this method is not without its challenges and limitations, including subjectivity of ratings, limited social connections, potential for manipulation, and incomplete representation of identities.

The innovative startup Shegby addresses some of these challenges through the concept of the Network State, as proposed by Balaji Srinivasan[1]. The Network State concept helps mitigate the limitations of social connections and incomplete representation by harnessing the network effect built into Shegby’s algorithm and services. By emphasizing trust and assuming good faith, Shegby promotes an environment where participants act honestly and productively.

Key Points:

1. Subjectivity of Ratings: Rankings based on social connections can be subjective and dependent on others’ opinions, potentially distorting results.
2. Limited Social Connections: Individuals with fewer social connections may find it difficult to verify their identity through others.
3. Possibility of Manipulation: The Human Rank method can be manipulated if participants misuse their social connections to improve their rank.
4. Incomplete Representation: Some people belong to different social networks that do not intersect, leading to incomplete identity representation.

Strategies to Address Challenges:

1. Trust Consideration: Incorporating the trustworthiness of rating sources to reduce subjectivity and prevent manipulation.
2. Additional Verifications: Implementing document verification or biometric methods to enhance reliability.
3. Network Expansion: Encouraging participants to expand their social networks for better identification accuracy.
4. Control and Transparency: Ensuring transparency in the Human Rank calculation process to prevent abuse.

Role of the Network State: Shegby’s implementation of the Network State concept leverages the network effect to overcome social connection limitations. It places trust at the forefront, operating on the presumption of good faith, where each participant is assumed to:

• Have a meaningful and constructive position.
• Have no ill intentions.
• Be inclined towards productive and mutually beneficial interactions.

Assuming Good Faith: This principle is fundamental in creating a collaborative environment. It assumes that people are not intentionally trying to harm the project, even when their actions may appear harmful. This mindset is crucial for the success of platforms like Wikipedia[2] and is equally applicable to Shegby’s vision.

Vision of Shegby: Shegby believes that most people have good intentions towards themselves, others, and their surroundings. However, many do not know how to express these good intentions due to the influence of existing systems and environments. By developing Human Rank as a mechanism for future Network States, Shegby aims to change existing systems and demonstrate the effectiveness of assuming good faith, thereby transforming interaction environments globally.

The Human Rank method, supported by the self-identification approach and the Network State concept, offers a robust framework for decentralized identity verification. While acknowledging its potential challenges, the integration of trust, additional verification methods, and network expansion strategies can mitigate these issues. Shegby’s[3] emphasis on the presumption of good faith and constructive engagement sets a new standard for identity verification, fostering a more secure, private, and reliable system for the digital age. This approach not only enhances individual control over personal data but also paves the way for more trustworthy and efficient interactions in various sectors, from social networks to financial institutions and beyond.

Footnotes

[1] https://thenetworkstate.com/ by Balaji Srinivasan;
[2] https://en.wikipedia.org/wiki/Wikipedia:Assume_good_faith;
[3] https://linktr.ee/shegby — Shegby is the Web3 KYC SaaS for EVM blockchains (essentially the Network State).