Trust Graph Layer
Quantified trust and risk modeling across identity networks.
The Trust Graph computes structured trust scores using credential validity, relational data, and historical activity.
Quantified Institutional Trust Modeling
The Trust Graph Layer models relationships between identities and credentials in a graph structure. It computes risk-weighted trust scores using defined propagation logic.
Trust becomes measurable, auditable, and policy-aware.
Architecture Overview
Core Components
Operational Flow
Credential or activity event recorded.
Graph updated.
Trust score recalculated.
Output exposed to policy engine.
Trust Scoring Mechanisms
Trust scores are computed from multiple weighted signals. The scoring engine evaluates credential status, relationship strength, and behavioral patterns.
Credential-Based Trust
Valid credentials from recognized issuers contribute positively to trust scores. Credential type, issuer authority, and verification history are weighted factors.
Relationship Trust
Trust propagates through known relationships. Direct connections carry more weight than indirect paths. Relationship age and interaction frequency factor into calculations.
Behavioral Trust
Historical activity patterns inform trust assessments. Successful transaction history, compliance record, and network participation contribute to overall scores.
Temporal Decay
Trust scores decay over time without activity or credential updates. Recent activity carries more weight than historical data. Decay functions are configurable per use case.
Propagation Logic
Trust propagates through the identity graph using weighted path analysis. The propagation engine applies attenuation rules to prevent unrestricted trust distribution.
Direct Path Scoring
First-degree connections receive full trust weight. The scoring engine validates the relationship strength and applies the base trust coefficient.
Multi-Hop Attenuation
Trust diminishes with graph distance. Each hop applies an attenuation factor. Maximum path length limits prevent weak signals from influencing scores.
Path Aggregation
Multiple paths between identities strengthen trust scores. The aggregation function combines path weights while preventing double-counting.
Negative Signals
Revoked credentials, failed verifications, and policy violations generate negative trust signals. These propagate through the graph with decay functions.
Graph Structure
The graph database stores nodes representing identities, credentials, and organizations. Edges capture relationships, endorsements, and verification events.
Node Types
- Identity Nodes: DIDs representing individuals, institutions, or devices
- Credential Nodes: Issued verifiable credentials with status metadata
- Organization Nodes: Institutional entities with governance authority
- Transaction Nodes: Recorded activity events for behavioral analysis
Edge Types
- Issuance: Credential issued to identity by organization
- Verification: Credential presented and verified by third party
- Endorsement: Identity vouches for another identity
- Transaction: Financial or data exchange between identities
Vertical Mapping Example
Institutional Risk Scoring
Participant trust scores dynamically adjust authorization thresholds and transaction limits across institutional operations.
Implementation Benefits
- Real-time risk assessment based on credential status and transaction history
- Dynamic authorization thresholds adjust to participant trust score
- Transaction limits scale with demonstrated reliability
- Network effects reduce counterparty risk
- Compliance violations propagate as negative trust signals
Operational Flow
1. Onboarding: Institutional participant presents KYC credentials, initial trust score calculated
2. Transaction Activity: Successful settlements increase trust score, failed transactions decrease it
3. Risk Adjustment: Trust score triggers authorization threshold changes automatically
4. Network Effects: Relationships with high-trust institutional counterparties improve participant score
5. Policy Enforcement: Low trust scores trigger enhanced verification requirements
Threshold Interface
Organizations define trust thresholds that trigger policy actions. The threshold interface exposes trust scores to business logic and access control systems.
Policy Rules
Business rules map trust score ranges to operational outcomes. High-trust identities receive expedited processing. Low-trust identities face additional verification steps.
Dynamic Thresholds
Threshold values adjust based on market conditions or risk environment. Automated threshold management responds to emerging threats or compliance requirements.
Audit Trail
All threshold decisions are logged with supporting trust calculations. Regulators can audit scoring logic and verify policy enforcement.
Privacy Considerations
Trust graph operations balance transparency with privacy. Scoring algorithms run on encrypted data where appropriate. Individual identities are pseudonymous unless explicitly revealed.
Selective Disclosure
Trust scores can be shared without revealing underlying credential details. Zero-knowledge proofs enable threshold verification without exposing exact scores.
Aggregation Privacy
Graph queries return aggregated statistics without identifying individuals. Differential privacy techniques prevent inference attacks on sensitive relationships.
Consent Management
Identities control which relationships are visible in the trust graph. Consent policies determine how trust scores can be shared with third parties.
Technical Integration
Organizations integrate trust scoring through APIs and event streams. The trust graph exposes RESTful endpoints for score queries and webhook notifications for threshold crossings.
Query API
Real-time trust score retrieval for identity verification workflows. The API accepts identity identifiers and returns current trust metrics with confidence intervals.
Event Streams
Subscribe to trust score changes for monitored identities. Event streams deliver notifications when scores cross configured thresholds or significant graph changes occur.
Batch Processing
Bulk trust score calculation for portfolio risk assessment. Batch APIs enable efficient processing of large identity sets for compliance reporting.
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