Preface

When it comes to the discussion of anti-Sybil, from the earliest Uniswap to the recent Celestia airdrop, it is almost everywhere and will continue to exist as a long-term narrative in the foreseeable future. It may be repeatedly mentioned in various narratives related to on-chain reputation-related DIDs, DeFi, SocialFi, GameFi, and other narratives. However, in the development history of Web3, the concept of anti-Sybil referred to in different textual contexts seems to be somewhat vague, relatively scattered, and confusing. This article attempts to use "anti-Sybil" as a reference point to outline and sort out its development stages, emerging new narratives, application scenarios, and various emerging solutions. Hopefully, the perspective of this article can also provide inspiration or assistance to friends who are concerned about the aforementioned related issues.

Compared to the hot changes in Web3 every week, the Sybil attack is not a very trendy concept; it is also very beginner-friendly, and basically, a newcomer to the circle has the opportunity to encounter and then actively seek information to understand and master the term. There are three driving factors for writing this research report: first, I was looking at the DIDs and social graph tracks some time ago and felt that the "looking at the future from the present" approach might be a bit difficult to grasp for some projects' commonalities, core, and differences, but observing the current narrative trend by retracing a historical stage might feel clearer and more interesting; second, compared to the previous rounds of airdrop projects, Celestia's anti-Sybil rules have clearly been upgraded; third, I was recently asked by a friend's soul-searching question: "Why hasn't the Sybil problem that Web2 couldn't solve been solved in Web3?"

I. What do we mean when we talk about the Sybil attack?

1. Classic Concept Review

• Anti-Sybil, also known as witch hunting or anti-Sybil attack. The Sybil attack generally refers to the violation of a real person controlling multiple false identities in order to repeatedly receive incentives provided by ecosystem builders, which is an extension and expansion of the Web2 concepts of "anti-cheating" and "anti-fraud" in Web3. Simply put, it is when a person disguises themselves as several users to repeatedly "milk" the project team. A more easily noticeable related phenomenon is the emergence of numerous "milk studios."

• According to popular belief, the reason why it is called the Sybil attack is because there is a movie with a female protagonist who has dozens of personalities called "Sybil," and it is considered very apt to use it to refer to a person controlling multiple false identities.

• Sybil attacks in the Web2 era: Didi was once cheated by drivers with empty orders, video platforms that make you watch ads to earn commissions actually generate false user click data using brand advertisers' ad budgets, and star fans control public opinion for crisis PR, these are all Sybil attacks in the Web2 era.

2. User demand or anti-user demand?

• From the perspective of users or community ecosystem participants, the first premise to consider is whether the community's demand for anti-Sybil is really strong enough. It needs to be acknowledged that, at the current stage, anti-Sybil is not a direct demand of users, but more of a demand from application scenario project teams. It can even be said that the "milk hunters" may have contributed a large amount of TVL in the early stages. Taking the airdrop scenario as an example, users actually have a strong motivation to become a witch, but in the process of weighing the pros and cons with the reward and punishment rules set by the project team, most users with the potential risk of being a witch ultimately step back and become airdrop hunters.

• For project teams, not distributing tokens to real users has two imaginable consequences: doubling the cost of marketing and attracting new users, and a huge selling pressure.

• For example, Aptos did not implement anti-Sybil measures. When the airdrop news came out, there were screenshots of milk hunters showing hundreds of accounts applying for the test network on Twitter and in the community. So, on average, the project team actually spent tens of times the unit cost to acquire a real new user. Second, from the trading trend on Binance, the Aptos community was not able to withstand the huge selling pressure, and the price of the token plummeted after it went online, with about 40% of the tokens flowing into the exchange coming from witch addresses. Therefore, knowing the proportion of witch addresses in advance is also very helpful for predicting the price fluctuations of project tokens.

• We can do a simple game analysis to understand the potential impact of the project team's anti-Sybil measures on real users: suppose Alice is a real user with only one wallet address for interaction; Bob is a witch who controls N fake accounts, with an additional unit cost of F. Before the airdrop, users need to invest time, effort, and money in tasks, earning points, and paying gas fees, denoted as X. The airdrop standard set by the project team is Y, and the airdrop income for a single account is Z. The net income from the Sybil attack is simply N*(aZ-bX-cY-dF). Typically, project teams will choose to increase X, Y, or decrease Z to reduce the ROI of the Sybil attack. However, when the project team's anti-Sybil measures against Bob are not precise enough or too strict, it may significantly reduce or even result in negative airdrop income (aZ-bX-cY) for real user Alice. This also explains why some airdrops received a lot of community praise, while others led to a lot of dissatisfaction.

• Currently, the balance between "anti-Sybil" and "milk hunting" usually has two equilibrium states, with the opportunity to achieve positive-sum games rather than the commonly perceived zero-sum games:

▪ (1) Default mild milk hunting behavior. That is, tacitly allowing the use of 1-3 high-quality wallets to receive airdrops for airdrop hunters. However, witch behavior is severely cracked down upon, i.e., users who own hundreds of wallets, most of which are operated using scripts or fingerprint browsers, occasionally manually operated.

▪ (2) Co-creating a "false" prosperity. On the one hand, from the perspective of the project team, milk hunting behavior is an important part of the active data on the project chain and can identify various bugs in the project in the early stages, promote product experience optimization, and effectively help with stress testing. Most project teams also need milk hunters to contribute data to increase valuation or list on CEX. From the perspective of milk hunters, they can also receive token airdrops in the future, making it a win-win situation.

II. How far have we pushed the progress bar for mature anti-Sybil solutions?

To fully understand this issue, we first need to realize that anti-Sybil actually has three layers of narrative structure. Then we will find that even before the prevalence of airdrops and milk hunters, the scenarios and solutions for the Sybil attack already existed, and some of the later emerging narratives and discussions are actually continuations of anti-Sybil.

1. Three-layer narrative structure of anti-Sybil: Application scenario layer, protocol consensus layer, and verification detection layer

The anti-Sybil-related tracks are mainly divided into three layers: application scenarios, protocol consensus, and identification detection. The application scenario layer determines the value target of anti-Sybil, the protocol consensus layer determines the institutional rules of anti-Sybil, and the verification detection layer determines the technical efficiency of anti-Sybil.

1.1 Application Scenario Layer: Where does the witch appear?

Mining

The "mining" here refers to the narrow sense under the PoW consensus mechanism.

• Value target: Maintain a proportional supply of verification computing power and control over the main chain. Under the Proof of Work (PoW) consensus mechanism, an attacker who controls the majority of computing power can conduct fraudulent operations in the blockchain network. The witch's goal may be to launch a 51% attack to create a new main chain, then carry out fraudulent operations such as revoking, reversing, or not confirming transaction records on the old main chain, and achieve double spending arbitrage on the new main chain.

• Related solutions: It is usually dependent on increasing the scale of the blockchain network to provide natural defense. Witch attacks generally occur on small chains, and the high computing power and hardware resources required for large chains make the ROI of witch attacks very low, making it unprofitable and naturally reducing the frequency of witch attacks and security threats. In this sense, top-tier public chains (such as BTC) naturally have a basic advantage in low-cost defense against witch attacks compared to other PoW-based small chains; other related concepts: decentralized distributed computing power.

Voting

•Value target: Maintain a proportional staking rate and voting rights. Under the Proof of Stake (PoS) consensus mechanism, the form of the Sybil attack is through the purchase of governance tokens to intervene in voting. Voting rights or decision-making rights are usually related to the amount of tokens held by the user, so attackers who gain a large number of tokens through a Sybil attack may disproportionately vote for an unreasonable proposal, thereby manipulating the voting results. This may lead to the "bribery efficiency" of specific proposals being artificially increased or decreased, and the allocation of resources in the project being biased towards a specific group. In simple terms, a whale can gain over 51% of the voting rights by purchasing a large number of governance tokens and depositing them into seemingly unrelated multiple wallets, thus dominating the voting results.

•Related solutions: First, similar to the PoW consensus mechanism, Sybil attackers cannot launch an attack by simply registering new wallet addresses, but need to invest in purchasing a large number of governance tokens. Therefore, larger chains are more likely to resist low-cost Sybil attacks compared to smaller chains. Second, the Proof of Person (PoP) consensus mechanism can address this issue. The core idea is to ensure that each participant in the network has only one identity or voting right. Unlike traditional blockchain consensus mechanisms such as Proof of Stake or Proof of Work, PoP focuses on the uniqueness of individual identities rather than the weight based on economic resources (such as coin age, token holdings, or computing power).

Airdrops

•Value target: Distribute tokens to genuine early contributors to maintain a proportional increase in users and marketing costs. Airdrops are a common user growth tool used by project teams for marketing and attracting new users, and are currently the main battleground for anti-Sybil actions. As mentioned earlier, not distributing tokens to genuine users has two imaginable consequences for the project team: doubling the cost of marketing and a huge selling pressure.

•There are mainly two types of airdrops: (1) Push airdrops: generally refers to tokens or NFTs appearing in users' wallets through full delivery; (2) Pull airdrops: users actively claim rewards, including Uniswap, ENS, 1INCH, and Cow Swap, among others. Push airdrops are easily used as a means of fraud, so the majority of project team airdrops adopt the Pull mode.

•Related solutions: Reputation scoring or identity verification (Gitcoin Passport, DegenScore, Otterspace, Trusta Labs); biometric verification (Humanode, Worldcoin); KYC, SBT, Poap, Proof of human, and other DID solutions.

Donations

•Value target: Maintain a proportional public goods rating and donation amount. For example, Quadratic Funding for public goods is a matching donation mechanism that balances donation incentives and decentralization. Although Quadratic Funding reduces the impact of large donations on the matching pool's rating for public goods, it provides an initial algorithm design to balance donation incentives and decentralization. However, it is very susceptible to Sybil attacks. The reason is that registering multiple accounts can have a smaller total fund allocation impact with less funding. Additionally, even though project audits, donor authentication, and data tracing may reduce the success rate of Sybil attacks, there is still the possibility of "witch poisoning":

￮Witch poisoning: Attackers use witch accounts to make small donations to competitors or projects at the top of the leaderboard, causing the project to be identified as a "witch" by data analysis teams, thereby preventing the project from receiving matching donations. This not only undermines competitors but also redirects the canceled matching donations back to the matching pool, allowing the attacker's own project to receive more matching donation amounts. Once this type of attack is successful, extortion by witch accounts against projects may also occur.

•Related solutions: Reputation scoring or identity verification solutions such as GitCoin Passport.

Universal Basic Income (UBI)

•Value target: Distribute UBI funds to genuine members of the organization to maintain a proportional distribution of member benefits and fund expenditures. Universal Basic Income (UBI) is also known as Unconditional Basic Income. UBI is not a native Web3 proposal and has appeared multiple times in governance and national welfare proposals in high-welfare countries such as Norway, the United States, and Germany. It usually refers to unconditional, unqualified, non-means-tested, and non-work-related regular monetary payments to every citizen or community member. In simple terms, UBI is an indiscriminate minimum guarantee distributed to organization members.

•Solution: Mainly based on biometric identification technology for Proof of Person (PoP). For example, Worldcoin requires iris scanning at offline sites to receive the Worldcoin Grant, which is to prevent a real person from controlling multiple accounts to avoid repetitive Grant distribution. Worldcoin claims to store only the irreversible hash value of the iris information rather than the iris photo itself to verify the uniqueness of user identity (World ID), thereby overcoming the privacy and security issues of traditional KYC.

•Related solutions: Worldcoin's iris verification

Conclusion

•Although the specific manifestations in different application scenarios may vary, it is easy to see that the value target of anti-Sybil can be summarized as a network security mechanism that maintains the cost of project value targets and the personal benefits of organization members in proportion. It is mainly related to preventing users from applying for a large number of fake accounts at the performance layer.

•If project teams cannot provide trustworthy commitments and technical proofs to community members in combating Sybil attacks, the long-term existence of Sybil attacks will damage the security and credibility of the project. Existing users and potential new users may reduce their participation or completely avoid investing in the project, which may lead to a series of chain problems at the application layer, such as miner attrition, decreased staking rates, overestimation of real user numbers, insufficient donation incentives, and excessive issuance of universal basic income.

1.2 Protocol Consensus Layer: Why did some anti-Sybil actions receive a lot of community praise, while others led to a lot of dissatisfaction?

Although creating a new wallet address does not require any cost, it may seem easy for a Sybil attack to occur. However, at the protocol consensus layer, there are already certain features that resist low-cost Sybil attacks. For example, the PoW mechanism requires each node to contribute computing power to participate in consensus; the PoS mechanism allocates voting rights based on the amount of tokens staked by validators. Therefore, users cannot increase their influence simply by creating new addresses. However, the basic consensus mechanism only increases the cost of Sybil attacks, and when the benefits of a Sybil attack far exceed the cost, malicious actors will still choose to launch an attack. Therefore, there are many innovations at the consensus mechanism level, such as the Proof of Person mechanism (PoP), Delegated Proof of Stake (DPoS), Proof of Authority (PoA), etc. There are also distributed protocols developed with inherent protection against Sybil attacks, such as algorithms like SumUp and DSybil, but these will not be specifically discussed here.

In addition, the project team's rule measures that affect the ROI of Sybil attacks may also change the efficiency of anti-Sybil. Generally, regardless of the application scenario, the community's evaluation of anti-Sybil measures is usually related to the size of the project's funding pool, token distribution formula, and qualification criteria.

1.2.1 Funding Pool Size

The funding pool size refers to the amount of funds available for rewards and incentives in the project. Because the size of the funding pool directly affects the potential return for each participant. When the funding pool is large, community members may generally be more optimistic, but at the same time, there is a greater potential risk of being targeted by Sybil attacks.

For example, the funding pool for OP's second airdrop accounts for only 0.27% of the total token supply, which is a drop in the bucket compared to the 5% for the first airdrop. The small funding pool has sparked widespread controversy in the community.

1.2.2 Token Distribution Formula

The token distribution formula determines how participants receive token rewards. This includes how each participant's contribution is calculated and the rules for distributing tokens based on these contributions. Transparent and fair distribution formulas are usually well-received by the community because they ensure that everyone's efforts receive a fair reward. However, if the distribution formula is complex, opaque, or biased, it may lead to dissatisfaction and controversy among community members.

For example, the second airdrop allocation of OP focuses on governance, while also taking into account active users who do not participate in governance. By refunding GasFee, the "airdrop farmers" who do not participate in Optimism governance have no profit in this airdrop. Most users did not receive good profits in this round of airdrops, which is the main reason why this round of airdrops did not generate wealth effects. Most addresses received airdrop incentives of less than 50 $OP, and only a few addresses received incentives of over a hundred $OP.

1.2.3 Qualification Criteria

Qualification criteria determine the standards for who is eligible to participate. These criteria may include completing specific tasks, interacting with the project multiple times, or even an identity verification process (such as KYC). If the qualification criteria are too strict or cumbersome, it may prevent some community members from participating, leading to dissatisfaction. On the other hand, if the criteria are too lenient, it may not effectively prevent abuse, thereby weakening the effectiveness of anti-Sybil measures.

For example, to qualify for the SEI airdrop, users not only need to interact multiple times and complete five tasks, but also need to fill out forms multiple times and undergo KYC. The complex process and the risk of privacy exposure have sparked community dissatisfaction.

1.3 Verification and Detection Layer: Technical Path Differences and Pros and Cons of Solutions

While reducing the cost-effectiveness of Sybil attacks through the protocol consensus layer is a good idea, the anti-Sybil effect cannot guarantee complete eradication, and it is difficult for project teams to make accurate judgments and verifications at this layer. Therefore, we also need incentive mechanisms or technical solutions for verifying and detecting Sybil accounts, such as community detectives, identity credentials & Proof of Personhood (PoP), and on-chain behavior activity analysis. It is worth noting that each identified list of historical Sybil addresses will also be inherited and adopted by new project teams.

1.3.1 Community Detectives

•Safe, HOP, CONNEXT, and Optimism encourage community detectives to report Sybil accounts, transferring the responsibility of anti-Sybil to the entire community. Although the intention is good, "Report to earn" is also a novel user incentive method, but this practice has sparked community controversy. For example, the reported Sybil accounts of CONNEXT even threatened to poison other wallets, which could undermine the community's anti-Sybil efforts.

•Advantages: Generally, community detective intelligence has timeliness and diverse expertise.

•Disadvantages: Poor accuracy, low success rate of reports, easy to miss Sybil accounts, or trigger retaliatory poisoning by Sybil accounts.

1.3.2 Identity Credentials & Proof of Personhood (PoP)

There are many solutions in the direction of identity credentials & Proof of Personhood, usually involving several concepts of decentralized identity (DIDs), such as Soulbound Tokens (SBT), Verifiable Credentials (VC), and Proof of Attendance Protocol (POAP).

1. Soulbound Tokens (SBT):

•SBT is a special type of non-fungible token (NFT) that represents individual identity, attributes, or achievements. Unlike ordinary NFTs, SBTs cannot be traded or transferred because they are bound to the holder's "soul" (i.e., personal identity). They are used to express non-transferable attributes in the digital world, such as educational qualifications, work experience, or community reputation.

•Advantages:

￮ Personalization and non-transferability: SBTs are closely tied to individual identity and cannot be transferred or traded, increasing their accuracy in representing personal identity and qualifications.

￮ Flexibility and scalability: Various types of SBTs can be created for different verification purposes as needed.

•Disadvantages:

￮ Privacy issues: SBTs closely related to individuals may expose too much personal information.

￮ Difficulty in revocation and modification: Once issued, SBTs may be difficult to revoke or modify, which may be problematic when information needs to be updated.

2. Verifiable Credentials (VC)

•VCs are digital certificates used to prove the holder's attributes or qualifications. These certificates can be issued by various institutions or individuals and can be used to verify identity, qualifications, membership, or any other verifiable information. The key feature of VCs is that they can be used in a decentralized environment without relying on a single issuing authority.

•Advantages:

￮ Better privacy control: VCs allow holders to choose which information they want to share, enhancing privacy protection.

￮ Wide applicability: VCs can be used in various scenarios, from identity verification to qualification certification.

￮ Easy to manage and update: Compared to SBTs, VCs are easier to update or revoke.

•Disadvantages:

￮ Centralization: The trustworthiness of VCs highly depends on the reputation and reliability of the issuing party.

3. Proof of Attendance Protocol (POAP)

•POAP is a digital collectibles system used to verify records of individual participation or attendance in a specific event or activity. This concept is becoming increasingly popular in the cryptocurrency and blockchain community, especially as a confirmation of participation in various virtual and real-world events.

•Advantages:

￮ Uniqueness and tamper resistance: As a blockchain-based NFT, POAP has uniqueness and tamper resistance. This helps ensure that each POAP represents a genuine participation event, reducing the likelihood of forgery or duplicate identities.

￮ Verification of participation: POAP can be used as evidence of a user's participation in an event. This method is particularly useful in scenarios that require verification of genuine participants, such as voting.

￮ Increased attack cost: To successfully execute a Sybil attack, the attacker needs to obtain a large number of POAP, which may involve actual participation in multiple events or finding other ways to obtain these POAP. This increases the cost and complexity of executing the attack.

￮ Transparency and traceability: Since POAP records are on the blockchain, their issuance and transfer are transparent and traceable, helping to identify and prevent malicious behavior.

•Disadvantages:

￮ High cost: On certain blockchains, such as ETH, creating and transferring NFTs may require expensive transaction fees.

Other identity credential solutions: KYC, biometric verification, OP's on-chain proof AttestationStation, and others.

1.3.3 On-chain Behavior Activity Analysis

AI-ML algorithms can resist Sybil attacks by monitoring on-chain behavior patterns. However, the difficulty of detecting Sybil lies in the lack of labels for real Sybil accounts, making it impossible to construct a suitable training set for supervised learning and extrapolate to a test set to check the accuracy of the algorithm's judgment. For example, the algorithm may not only miss some Sybil accounts but also mistakenly identify a genuine account as a Sybil account, leading to biased airdrop lists and community dissatisfaction. Therefore, current anti-Sybil AI-ML algorithms mainly rely on time series analysis and graph mining based on on-chain data, such as analyzing batch transfers and interactions, mining behavior sequence patterns, and asset transfer graphs, gradually accumulating real Sybil labels through manual verification.

•Batch Transfers and Interactions: Detecting Sybil accounts by analyzing transaction behavior between different addresses, including identifying batch transfers and interaction patterns that may indicate Sybil behavior.

•Behavior Sequence Pattern Mining: Mining data to find continuous behavior patterns that may indicate Sybil attacks.

•Asset Transfer Graph (ATG): Using asset transfer graph mining to analyze the flow of assets between entities, helping to identify potential Sybil attacks.

For example, Celestia's airdrop adopted Trusta Labs' anti-Sybil solution, using a two-phase on-chain behavior analysis to identify Sybil accounts.

•Phase 1: Community detection on ATG. Using community detection algorithms such as Louvain and K-Core to analyze the asset transfer graph (ATG) to detect dense connections and suspicious Sybil groups.

￮Starburst Attack: Addresses within the group receive transfers from the same source address.

￮Star Convergence Attack: Addresses within the group send funds to the same target address.

￮Tree Attack: The fund transfer relationships within the group form a tree-like topology.

￮Chain Attack: Sequential transfers between addresses form a chain-like structure.

•Phase 2: K-Means Optimization Based on Behavioral Similarity. Calculate user profiles and activities for each address. K-means optimizes clustering by filtering different addresses to reduce false positives from Phase 1.

Image Source: Introduction to Trusta Airdrop Anti-Sybil Framework: A Reliable Solution Based on AI and Machine Learning

•Advantages:

1. AI-ML protects user privacy. Users do not need to provide personal biometric information and a series of identity verifications in Web2. Proof of Personhood requires identity confirmation, sacrificing user anonymity.

2. AI-ML comprehensively analyzes a large amount of on-chain data to reduce vulnerabilities, making the solution more reliable. Identity verification can be exploited and forged, making Proof of Personhood vulnerable to attacks.

3. The AI-ML solution is essentially permissionless, allowing anyone to analyze the same public data.

4. Due to the transparency of AI-ML analysis, Sybil identification can be made public for multi-party verification.

•Disadvantages:

1. The lack of real Sybil labels limits the complexity, accuracy, and recall rate of AI-ML algorithms. In 2022, there is no benchmark dataset labeled as Sybil available for training supervised models. Training on static Sybil/non-Sybil data may result in fragile accuracy and recall rates. Since a single dataset cannot cover all Sybil patterns, the recall rate is limited. Additionally, misclassified users cannot provide feedback, hindering further improvement in accuracy.

2. Most Sybil behaviors do not have significant differences from regular users, making identification difficult. Currently, clustering algorithms are the most suitable detection method. With the accumulation and confirmation of real Sybil labels, more mature deep learning algorithm frameworks can be adopted to optimize detection methods in the future.

2. What New Narratives Have Emerged from Anti-Sybil Measures?

Although the current anti-Sybil narrative seems to be driven by the needs of project teams, the philosophical validity of the "anti-user" nature of it is questionable. However, there is every reason to believe that it may expand into more robust ecological values in future narratives. One of the extended narrative directions is DeFi and credit lending. Previously, blockchain credit lending was almost blank due to the lack of an on-chain credit assessment system, leading to the majority of lending using over-collateralization mechanisms. The construction of a credit assessment system needs to be based on the foundation of excluding false accounts. Another emerging narrative direction is SocialFi and social graphs. In the content creation ecosystem based on social graphs and on-chain reputation (such as SocialFi and GameFi), once social influence can be reasonably priced, Sybil attacks may manipulate reputation scores, comments, or social influence to profit, similar to the use of fake accounts and bots to manipulate reviews on Web2 social media. Therefore, in DeFi, SocialFi, and GameFi scenarios, we may see user-driven application scenarios extending from anti-Sybil measures.

•Diversification of Address Credit Scoring Systems: Integration of Multiple Detection Solutions

Integrating multiple detection solutions to balance privacy and accuracy. While binding identity credentials or providing biometric information can more effectively distinguish real users from fake accounts and can leverage many mature Web2 solutions, it also comes with higher user information collection requirements. Conversely, AI-ML-based solutions have advantages such as large-scale detection, multi-party verification, and privacy protection, but they also face the issue of not being able to intercept in advance or in real-time. Therefore, integrating multiple detection solutions is a better solution.

•Users Having a Main Address and Multiple Sub-Addresses Managed by Wallets

The relationship between reputation binding and identity loss needs to be balanced. In the future, each real user may only have one main address to remember, identifying a sustainable accumulation of reputation (partially overlapping with DIDs), and users can only receive incentives provided by project parties through the main address, such as airdrops. However, they can associate multiple local addresses for interactions with application projects, preventing systemic risks due to the loss or theft of the main address.

Conclusion

Based on the above analysis, we can now address some of the questions raised in this article. Firstly, the anti-Sybil measures in Web3 are an extension of the anti-fraud and anti-cheating concepts in Web2, but they also incorporate some native features of Web3. For example, after the widespread promotion of airdrop narratives, the incentives for Sybil attacks have significantly increased. Due to the distinct characteristics of the game between "project parties" and "freeloaders," the verification and detection measures of witch-hunting actions have shifted from mild to severe and complex, and current technological methods are still being continuously upgraded. Secondly, although the current anti-Sybil narrative seems to be driven by the needs of project teams, the philosophical validity of the "anti-user" nature of it is questionable. However, there is every reason to believe that it may expand into more robust ecological values in future narratives in Web3, and subsequently fulfill user-driven value proofs, such as credit lending in DeFi, social graphs in SocialFi and GameFi, and so on. Currently, the technical solutions for anti-Sybil measures are transitioning from the embryonic stage to maturity, and the endgame form may involve users having a main address linked to a credit scoring system integrated with multiple detection solutions, and multiple sub-addresses used for interactions in vertical application scenarios and account recovery verification.

Finally, it is hoped that when readers encounter narratives related to anti-Sybil and on-chain reputation in future projects, they will clearly understand what the term "witch" specifically refers to in attempting to attack which value scenario using false identities. Are the project parties proposing a new protocol consensus to reduce the ROI of Sybil attacks at the source, discussing admission credentials or identity markers before an attack occurs, or talking about detection tools and methods after an attack occurs? Of course, related projects are likely to involve more than one layer. The game between anti-Sybil measures and freeloading will continue, but it is likely to be a positive-sum game rather than the zero-sum game usually assumed. Specifically, the primary investment logic is roughly as follows: application value prospects > verification and detection technology > protocol consensus innovation.

References

[1] https://medium.com/@trustalabs.ai/trustas-ai-and-machine-learning-framework-for-robust-sybil-resistance-in-airdrops-ba17059ec5b7

[2] https://passport.gitcoin.co/

[3] https://docs.lens.xyz/docs

[4] https://cyberconnect.me/

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