Reputation Aggregation

Aggregators pull data from diverse sources to create a holistic view. Key sources include:

• On-chain activity: Transaction history, token holdings, governance participation, and DeFi interactions.
• Off-chain attestations: Verifiable credentials, KYC/AML status, and social media proofs.
• Protocol-specific metrics: Historical performance within specific applications like lending or NFT marketplaces. This multi-dimensional approach prevents sybil attacks and gaming by requiring reputation to be earned across multiple vectors.

Raw data is processed through a scoring algorithm that assigns weights and calculates a composite score. This involves:

• Temporal decay: Recent activity is often weighted more heavily than older actions.
• Context-specific rules: A governance-heavy profile may score differently for a DAO than for a lending protocol.
• Transparent or opaque models: Algorithms can be fully open-source (verifiable but gameable) or private (harder to game but less transparent). The output is a normalized score, often a number or tier (e.g., Gold, Silver, Bronze).

A core innovation is the creation of a portable reputation asset. This can be:

• A non-transferable token (Soulbound Token) representing the score.
• A verifiable credential stored in a decentralized identity wallet.
• A cryptographically signed attestation from a reputed issuer. This portable proof allows users to leverage their established reputation across different dApps without starting from zero, enabling reputation composability across the Web3 stack.

Aggregation is a primary tool for sybil resistance. By requiring a costly or time-intensive history of genuine interactions, it raises the barrier to creating fake identities. Techniques include:

• Graph analysis: Identifying clusters of addresses controlled by a single entity.
• Liveness proofs: Requiring periodic, low-cost transactions to prove key control.
• Correlation with off-chain identity: Linking to verified credentials without exposing raw PII. This creates a scalable form of proof-of-personhood or proof-of-uniqueness.

Users maintain control over their data through zero-knowledge proofs (ZKPs) and selective disclosure. Instead of revealing all transaction history, a user can prove statements like:

• "I have a reputation score > X" without revealing the exact score.
• "I have held > 1 ETH for > 1 year" without revealing wallet balance or address.
• "I am KYC-verified by Provider Y" without sending the full document. This preserves privacy while enabling trustless verification.

Aggregated reputation unlocks new design patterns:

• Collateral-free lending: Credit scores based on on-chain history.
• Governance: Weighted voting based on contribution, not just token holdings.
• Access control: Gated communities or airdrops for proven users.
• Reduced gas auctions: Prioritizing transactions from reputable addresses.
• Workforce credentials: Verifiable proof of freelance work history on platforms like Gitcoin or Layer3.

A primary security concern is preventing Sybil attacks, where a single entity creates many fake identities to manipulate the aggregated reputation score. Common defenses include:

• Proof-of-Stake or Proof-of-Work requirements for identity creation.
• Social graph analysis to detect unnatural clusters of activity.
• Costly signaling mechanisms that make creating fake identities economically prohibitive.

The quality of the aggregated reputation is only as good as its inputs. Systems must verify the authenticity and integrity of underlying data. Key methods include:

• Using on-chain data (e.g., transaction history, NFT holdings) for verifiable provenance.
• Employing cryptographic attestations or oracles for off-chain data.
• Implementing slashing mechanisms to penalize providers of fraudulent data.

The choice of aggregation algorithm has major implications for fairness and manipulation resistance. Design trade-offs include:

• Weighted vs. Unweighted Averages: Should all inputs be equal, or weighted by source trustworthiness?
• Time Decay: Should older contributions carry less weight to reflect current behavior?
• Outlier Handling: How to mitigate the impact of maliciously extreme scores (e.g., median vs. mean).

Balancing transparency with user privacy is a key design challenge. Considerations involve:

• Selective Disclosure: Allowing users to prove reputation traits (e.g., "score > X") without revealing the full underlying data, using zero-knowledge proofs.
• Data Minimization: Aggregating only necessary signals to compute the score.
• Composability: Ensuring the aggregated reputation is a portable, standard (e.g., ERC-20/721-like) asset that can be used across different applications (DeFi, DAOs, Social).

Who controls the aggregation parameters is a centralization risk. Systems must design for:

• Parameter Control: Deciding who can update source weights, algorithms, or inclusion criteria.
• Immutable vs. Upgradeable: Whether the system's rules are fixed or can be amended, often via a DAO.
• Fork Resistance: Mitigating the risk of the reputation system splitting if the underlying protocol forks.

The system must be designed so that rational actors are incentivized to act honestly. This involves:

• Staking and Slashing: Requiring data providers or voters to stake collateral that can be slashed for malicious behavior.
• Value Alignment: Ensuring the reputation score is tied to an asset of real economic value, making manipulation costly.
• Iterative Design: Using mechanisms like Futarchy or conviction voting to continuously refine the aggregation model based on outcomes.

Critical for preventing fake identities from gaming reputation systems. Common techniques include:

• Proof of Personhood: Solutions like Worldcoin or BrightID verify unique human identity.
• Social Graph Analysis: Mapping connections between identities to detect bot networks.
• Stake-based Weighting: Requiring a financial stake (e.g., token deposit) that can be slashed for malicious behavior.
• Context-Specific Proofs: Using credentials from established Web2 platforms (e.g., Twitter, GitHub) as a bootstrap layer.

Reputation aggregates voting power and influence within DAOs and governance systems. It enables:

• Reputation-based Voting: Voting power is non-transferable and earned through contributions, preventing whale dominance.
• Smart Delegation: Token holders can delegate votes to experts whose reputation score reflects deep knowledge in a specific domain (e.g., treasury management, protocol security).
• Proposal Quality Filters: Systems can require a minimum reputation threshold to submit proposals, reducing spam.