Zero-Knowledge Social Graph

Users can prove specific attributes about their social graph without exposing the entire dataset. For example, a user can cryptographically prove they are friends with a specific person or have over 100 followers, without revealing the identities of all their connections or the exact count. This is powered by zero-knowledge proofs (ZKPs) like zk-SNARKs.

The social graph's state (e.g., connection lists, reputations) is maintained off-chain for efficiency and privacy. Only compact cryptographic commitments (hashes) and zero-knowledge proofs of state changes are published on-chain. This allows the blockchain to act as a verifiable, trustless ledger of social interactions without storing sensitive personal data publicly.

User identities and social graphs are not owned by any single application. They are tied to a user's cryptographic keypair (e.g., a wallet). This allows users to port their social capital—their connections, reputation, and history—across different dApps and platforms, breaking vendor lock-in. The graph is a user-controlled asset.

By anchoring proofs to on-chain verification, these graphs reduce reliance on central servers that can censor or manipulate data. They enable cryptographic Sybil resistance: applications can require proofs of unique humanity or existing social capital (e.g., "prove you have 5 connections from this trusted set") without a central authority, mitigating spam and bot attacks.

ZK social graphs provide verifiable social primitives that other smart contracts and dApps can consume. Examples include:

• Proof-of-Followership: A DeFi protocol grants a loan based on a proof of reputable connections.
• Private Voting: A DAO conducts a vote where members prove eligibility (holding a token or being part of a group) without revealing their individual choice or identity.
• Gated Access: A community grants entry based on a proof of membership in another, unrelated group.

Users can prove they hold a credential (e.g., a DAO membership, a POAP, or a Sybil-resistant token) without revealing which specific one. This enables private gated access to communities or services. For example, a user could prove they are a member of a specific Discord server's NFT community to access a token-gated chat, without exposing their wallet address or the exact NFT they hold.

ZKPs allow for private voting in DAOs and governance systems. A user can prove they are an eligible voter (e.g., hold a governance token) and have cast a valid vote, without revealing their identity or voting pattern. This prevents vote buying and coercion, as votes cannot be linked to specific wallets or individuals, enhancing the integrity of decentralized decision-making.

Projects can distribute tokens or rewards to real human users while filtering out bots. Users generate a ZK proof that they meet certain social graph criteria (e.g., are a unique person with a minimum number of legitimate social connections) without exposing their entire social network. This ensures fair distribution without requiring users to dox their social media profiles or on-chain activity.

Users can build a portable, private reputation score across different platforms. By generating ZK proofs about past interactions (e.g., successful loans repaid, positive peer reviews, contribution history), a user can prove they have a good reputation without revealing the specific platforms they used or the details of every transaction. This enables trustless underwriting in DeFi or access to premium features based on proven behavior.

Applications can facilitate connections between users based on shared traits or interests, privately. For instance, two users could discover they are both alumni of the same university or work in the same industry by each generating a ZK proof of that fact. The matching occurs cryptographically without either party—or the platform—learning the specific shared attribute unless both consent to reveal it.

ZK social graphs enable platforms to operate in regulated environments (like finance) by proving user eligibility for services (e.g., KYC/AML status, accredited investor status, jurisdictional compliance) without storing or processing sensitive personal data on-chain. A trusted issuer attests to a user's status off-chain, and the user presents a ZK proof of this attestation to access a dApp, maintaining regulatory compliance and user privacy simultaneously.

The graph is constructed using verifiable credentials (VCs) issued by users to attest to relationships. A user can cryptographically prove they have a connection (e.g., 'follows', 'endorses') to another user's public key without disclosing the other party's identity or the connection's context. This is achieved using zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) to generate privacy-preserving proofs.

ZK social graphs enable Sybil-resistant systems where reputation is portable and private. A user can prove they have a certain social graph 'score' or set of endorsements from reputable entities without exposing their entire network. This is critical for:

• Governance: Voting with proof of human/uniqueness.
• Airdrops & Access: Gating based on anonymous reputation.
• Credit Systems: Private attestation of trustworthiness.

The graph's state is typically managed off-chain or in a layer-2 environment for efficiency, with periodic commitments (hashes) posted to a base layer like Ethereum. Common structures include:

• Merkle Trees: To commit to a set of identity commitments or edges.
• Sparse Merkle Trees: For efficient non-membership proofs.
• Graph Databases: Storing edge data (relationships) with ZK-friendly representations. State transitions require ZK proofs of valid graph updates.

A core privacy principle where users prove specific attributes (e.g., "I am over 18" or "I follow this creator") without revealing their entire profile or connection list. This is achieved through zero-knowledge proofs (ZKPs) like zk-SNARKs, which allow for selective disclosure. For example, a user can prove membership in a DAO without revealing which DAO, or prove they have a certain reputation score without exposing their transaction history.

A significant security challenge where the structure of the social graph itself—who is connected to whom—can be inferred even when individual connections are hidden. Adversaries can use network analysis, timing attacks, or observe public proof generation events to deanonymize users. Mitigations include using differential privacy techniques when generating proofs or aggregating proofs from many users into a single batch to obscure individual activity.

Many efficient ZKP systems require a trusted setup ceremony to generate public parameters. If compromised, this setup could allow false proofs to be generated, undermining the entire system's security. Furthermore, the system's security rests on cryptographic assumptions (e.g., the hardness of discrete logarithms). A future breakthrough in quantum computing or cryptanalysis could break these assumptions, requiring migration to post-quantum cryptographic schemes.

Preventing fake accounts (Sybils) is critical for graph integrity. Zero-knowledge social graphs often rely on proofs of personhood or verifiable credentials from trusted issuers (e.g., government IDs, biometrics, or established web2 social accounts) to bind a cryptographic identity to a real human. The security of the entire graph depends on the issuer's attestation security and the unforgeability of the underlying credential, such as a Soulbound Token (SBT).

A key architectural decision with security implications. Storing proof verification logic and minimal attestations on-chain (e.g., on Ethereum) leverages the blockchain's security and decentralization but can be expensive. Storing the raw social graph data off-chain (in a decentralized storage network or a server) is cheaper but introduces data availability risks and reliance on external data providers. Hybrid models use verifiable data structures like Merkle trees to bridge the two.

Managing the lifecycle of credentials and keys is a critical operational security concern. Users must be able to revoke compromised or outdated attestations (e.g., after leaving a community). This requires secure and accessible revocation registries. Furthermore, the loss of a user's private key (which controls their graph identity) is irrecoverable in a non-custodial system, leading to permanent loss of social capital, highlighting the need for social recovery or multi-party computation (MPC) wallets.

A cryptographic method where one party (the prover) can prove to another party (the verifier) that a statement is true without revealing any information beyond the validity of the statement itself. This is the foundational technology that enables privacy in social graphs.

• Types: zk-SNARKs, zk-STARKs, Bulletproofs.
• Core Properties: Completeness, soundness, and zero-knowledge.
• Use Case: Proving you are connected to a specific person in a network without revealing who they are.

A tamper-evident digital credential whose authenticity and integrity can be cryptographically verified. In a social graph, connections and attributes (like 'friend' or 'colleague') can be issued as VCs.

• Issuer: The entity (or user) that creates and signs the credential.
• Holder: The user who stores and controls the credential.
• Verifier: The party that requests and cryptographically checks the credential's validity.

A self-sovereign identifier that is controlled by the user, not a central authority. DIDs are the foundational addresses for entities in a decentralized social graph, enabling portable identity across platforms.

• Structure: did:method:method-specific-identifier (e.g., did:key:z6Mk...).
• Control: Managed via cryptographic key pairs, not usernames.
• Role: Serves as the persistent, verifiable 'node' in a social graph to which credentials are bound.

A database that uses graph structures (nodes, edges, properties) to represent and store data. It is the underlying data model for any social graph, optimized for traversing relationships.

• Nodes: Represent entities (users, organizations).
• Edges: Represent relationships (follows, trusts, interacts).
• Query Language: Often uses languages like Cypher (Neo4j) or Gremlin for efficient relationship traversal, which is crucial for mapping social connections.

The ability for a user to reveal only specific, necessary attributes or claims from a credential or data set, while keeping the rest private. This is a key privacy feature enabled by ZKPs in social graphs.

• Example: Proving you are 'over 18' from a credential that contains your full birthdate.
• Social Graph Use: Proving you have 'more than 50 followers' without revealing who they are or your exact follower count.

The property of a network that makes it economically or computationally difficult to create a large number of fake identities (Sybils). A robust social graph requires mechanisms to ensure nodes represent unique, real-world entities.

• Methods: Proof-of-Personhood protocols, biometric verification, social attestation, or stake-based systems.
• Importance: Prevents spam, manipulation of reputation scores, and artificial inflation of influence within the graph.