The world needs anti-sybils, but if these solutions are not fully decentralized or private, they can easily create more harm than good.

Achieving both these attributes is however notoriously tricky. Anti-sybils only work when you can be sure that every user is unique. This creates obvious challenges. For instance, how does a system know you haven’t already registered without encroaching on your privacy?

The solutions are manifold, and all of them have trade-offs. Assessing these and gauging how private or decentralized any particular anti-sybil mechanism is while still being effective can be a minefield.

This guide is designed to make that process easier. If the solution you’re looking at falls into these categories these may be some of the strengths and weak points you can look out for.

[We use the term anti-sybil instead of proof-of-person firstly because it better captures the value these solutions provide (uniqueness), secondly, in the future, users will likely extend their anti-sybils to AI agents so that they can operate on their behalf]

Anti-sybil mechanism:

• Users create a profile and submit it to an on-chain registry

• Community-driven validation: users can challenge duplicate or fake profiles.

• Disputes are resolved by an arbitration service.

Pros

• Decentralized oversight: No single entity fully controls validation; disputes are handled collectively.

• Community-driven: The process incentivizes participants to keep the system honest by challenging suspicious profiles.

• Flexible and contextual: Human arbitrators can evaluate nuanced cases that automated checks might miss.

• Transparent: Decisions and dispute outcomes can be tracked on-chain, increasing accountability.

Downsides/Risks

• Subjective, manual review: Human validators can be inconsistent or biased.

• Time and effort: The challenge and validation process can be slow or labor-intensive.

• Privacy concerns: Your profile is becomes publicly viewable

• Potential gatekeeping: Even if the arbitrations service is decentralized, collusion can impact who gets verified.

Anti-sybil mechanism:

• Users scan their iris, finger print, or any other unique biometric

• The biometric data is converted into a unique hash; zero-knowledge proofs ensure uniqueness without storing raw data

Pros:

• High uniqueness: Biometrics are inherently harder to fake or duplicate.

• Automated verification: Reduces reliance on manual or community validation.

• Zero-knowledge privacy: Proper use of ZK proofs can ensure uniqueness without exposing the user’s actual biometric image.

Downsides/Risks:

• Hardware centralization: Typically relies on specialized proprietary hardware.

• Privacy concerns: Requires trusting the company’s handling of biometric data.

• Scalability and availability: Requires physical devices to be deployed worldwide.

• Regulatory risks: Biometric data collection often faces strict legal scrutiny.

Anti-sybil mechanism:

• Achievements and identities from across multiple platforms are aggregated, and their combined weight is used to attest to uniqueness

Pros:

• Leverages existing identities: Users can leverage the reputations they’ve already built on other platforms (e.g., Twitter, GitHub).

• Automated scoring: Pulling data from multiple sources can streamline the verification process.

• Lower barrier to entry: Users often already have social accounts, meaning no specialized hardware or official documentation is needed.

• Flexible: Different metrics (followers, activity, endorsements) can be combined for a more holistic identity check.

Downsides/Risks:

• Reliance on third-party platforms: Manipulating or buying social accounts can undermine the system.

• Social graph fragility: Public data can be faked or manipulated at scale.

• Centralized data sources: Major providers (Twitter, GitHub) can shut down APIs or change policies.

• Privacy: Linking multiple accounts can reveal personal information.

Anti-sybil mechanism:

• Users form a decentralized social graph by connecting offline with people they trust

• A user’s “score” improves as they connect to more verified individuals

Pros:

• Grassroots approach: Builds on personal trust relationships rather than third-party attestations.

• Decentralized: No single authority controls the network; trust emerges organically.

• Resilience through diversity: A wide variety of connections can be harder to fake than a single data point.

• Community engagement: Encourages users to actively validate and connect with one another.

Downsides/Risks:

• Network effects: New users need to connect with existing trusted nodes, which can exclude people without particular pre-existing social networks.

• Social graph manipulation: Collusion or fake networks (“Sybil rings”) can undermine the system.

• User experience: Requires active participation and building real social connections.

• Partial centralization: Seed communities or key members could exert undue influence.

Anti-sybil mechanism:

• A centralized service issues users with a verified credential after they submit proof of their identity

Pros:

• Simplicity: A straightforward, one-stop verification process (often standard KYC).

• Established trust frameworks: Reputable centralized services may already comply with known identity standards.

• Quick onboarding: Users can get verified credentials relatively fast compared to manual or community-based methods.

• Uniform procedures: Clear, consistent rules for what counts as a valid ID or proof of identity.

Downsides/Risks:

• Centralized verification: Civic acts as a trusted third party; a single point of failure could compromise user data.

• Privacy concerns: Requires sensitive personal information. Regulatory complexity: Compliance for personal data processing differs by jurisdiction.

• Exclusion risk: Users without valid are excluded, affecting inclusivity. 6. Rarimo's ZK Passport Registry

Disclosure, this is the method Rarimo uses.

Anti-sybil mechanism:

• You use passports or biometrics to self-issue your identity. This by generating ZK proofs of validity from your personal mobile phone

• Uniqueness is enforced by hashing identity metadata and adding it to an on-chain ZK Registry

• Users can prove uniqueness while maintaining anonymity by generating ZK proofs of inclusion in the registry

Pros:

• Trustless infrastructure: There are no trusted third parties or community consensus

• Scalability: There is no specialised hardware that needs to be distributed, and few prerequisites

• Strong privacy: The underlying personal information remains hidden, reducing the risk of data breaches

• Composability: ZK Registries can be used across multiple DApps and platforms without leaving a trace connecting them

Downsides/Risks

• ZKP complexity: Generating zero-knowledge proofs of identity validity is computationally intensive, potentially limiting accessibility for less powerful devices.

• Revocation challenges: Self-attested identities require careful revocation and recovery management to avoid disrupting linked digital identities.

Each solution addresses sybil attacks through different means (biometrics, social graphs, KYC, or community validation) but has its own trade-offs in terms of privacy, centralization, scalability, and inclusivity.

The world needs anti-sybils, but if these solutions are not fully decentralized or private, they can easily create more harm than good.

Achieving both these attributes is however notoriously tricky. Anti-sybils only work when you can be sure that every user is unique. This creates obvious challenges. For instance, how does a system know you haven’t already registered without encroaching on your privacy?

The solutions are manifold, and all of them have trade-offs. Assessing these and gauging how private or decentralized any particular anti-sybil mechanism is while still being effective can be a minefield.

This guide is designed to make that process easier. If the solution you’re looking at falls into these categories these may be some of the strengths and weak points you can look out for.

[We use the term anti-sybil instead of proof-of-person firstly because it better captures the value these solutions provide (uniqueness), secondly, in the future, users will likely extend their anti-sybils to AI agents so that they can operate on their behalf]

Anti-sybil mechanism:

• Users create a profile and submit it to an on-chain registry

• Community-driven validation: users can challenge duplicate or fake profiles.

• Disputes are resolved by an arbitration service.

Pros

• Decentralized oversight: No single entity fully controls validation; disputes are handled collectively.

• Community-driven: The process incentivizes participants to keep the system honest by challenging suspicious profiles.

• Flexible and contextual: Human arbitrators can evaluate nuanced cases that automated checks might miss.

• Transparent: Decisions and dispute outcomes can be tracked on-chain, increasing accountability.

Downsides/Risks

• Subjective, manual review: Human validators can be inconsistent or biased.

• Time and effort: The challenge and validation process can be slow or labor-intensive.

• Privacy concerns: Your profile is becomes publicly viewable

• Potential gatekeeping: Even if the arbitrations service is decentralized, collusion can impact who gets verified.

Anti-sybil mechanism:

• Users scan their iris, finger print, or any other unique biometric

• The biometric data is converted into a unique hash; zero-knowledge proofs ensure uniqueness without storing raw data

Pros:

• High uniqueness: Biometrics are inherently harder to fake or duplicate.

• Automated verification: Reduces reliance on manual or community validation.

• Zero-knowledge privacy: Proper use of ZK proofs can ensure uniqueness without exposing the user’s actual biometric image.

Downsides/Risks:

• Hardware centralization: Typically relies on specialized proprietary hardware.

• Privacy concerns: Requires trusting the company’s handling of biometric data.

• Scalability and availability: Requires physical devices to be deployed worldwide.

• Regulatory risks: Biometric data collection often faces strict legal scrutiny.

Anti-sybil mechanism:

• Achievements and identities from across multiple platforms are aggregated, and their combined weight is used to attest to uniqueness

Pros:

• Leverages existing identities: Users can leverage the reputations they’ve already built on other platforms (e.g., Twitter, GitHub).

• Automated scoring: Pulling data from multiple sources can streamline the verification process.

• Lower barrier to entry: Users often already have social accounts, meaning no specialized hardware or official documentation is needed.

• Flexible: Different metrics (followers, activity, endorsements) can be combined for a more holistic identity check.

Downsides/Risks:

• Reliance on third-party platforms: Manipulating or buying social accounts can undermine the system.

• Social graph fragility: Public data can be faked or manipulated at scale.

• Centralized data sources: Major providers (Twitter, GitHub) can shut down APIs or change policies.

• Privacy: Linking multiple accounts can reveal personal information.

Anti-sybil mechanism:

• Users form a decentralized social graph by connecting offline with people they trust

• A user’s “score” improves as they connect to more verified individuals

Pros:

• Grassroots approach: Builds on personal trust relationships rather than third-party attestations.

• Decentralized: No single authority controls the network; trust emerges organically.

• Resilience through diversity: A wide variety of connections can be harder to fake than a single data point.

• Community engagement: Encourages users to actively validate and connect with one another.

Downsides/Risks:

• Network effects: New users need to connect with existing trusted nodes, which can exclude people without particular pre-existing social networks.

• Social graph manipulation: Collusion or fake networks (“Sybil rings”) can undermine the system.

• User experience: Requires active participation and building real social connections.

• Partial centralization: Seed communities or key members could exert undue influence.

Anti-sybil mechanism:

• A centralized service issues users with a verified credential after they submit proof of their identity

Pros:

• Simplicity: A straightforward, one-stop verification process (often standard KYC).

• Established trust frameworks: Reputable centralized services may already comply with known identity standards.

• Quick onboarding: Users can get verified credentials relatively fast compared to manual or community-based methods.

• Uniform procedures: Clear, consistent rules for what counts as a valid ID or proof of identity.

Downsides/Risks:

• Centralized verification: Civic acts as a trusted third party; a single point of failure could compromise user data.

• Privacy concerns: Requires sensitive personal information. Regulatory complexity: Compliance for personal data processing differs by jurisdiction.

• Exclusion risk: Users without valid are excluded, affecting inclusivity. 6. Rarimo's ZK Passport Registry

Disclosure, this is the method Rarimo uses.

Anti-sybil mechanism:

• You use passports or biometrics to self-issue your identity. This by generating ZK proofs of validity from your personal mobile phone

• Uniqueness is enforced by hashing identity metadata and adding it to an on-chain ZK Registry

• Users can prove uniqueness while maintaining anonymity by generating ZK proofs of inclusion in the registry

Pros:

• Trustless infrastructure: There are no trusted third parties or community consensus

• Scalability: There is no specialised hardware that needs to be distributed, and few prerequisites

• Strong privacy: The underlying personal information remains hidden, reducing the risk of data breaches

• Composability: ZK Registries can be used across multiple DApps and platforms without leaving a trace connecting them

Downsides/Risks

• ZKP complexity: Generating zero-knowledge proofs of identity validity is computationally intensive, potentially limiting accessibility for less powerful devices.

• Revocation challenges: Self-attested identities require careful revocation and recovery management to avoid disrupting linked digital identities.

Each solution addresses sybil attacks through different means (biometrics, social graphs, KYC, or community validation) but has its own trade-offs in terms of privacy, centralization, scalability, and inclusivity.