Privacy Architecture

The Privacy Problem

Modern AI systems face a fundamental tension: they need context to be helpful, but collecting that context creates surveillance risks. Traditional approaches force users to choose between privacy and personalization.

VCP resolves this with a key insight: AI can be influenced by information without that information being transmitted. Private context shapes behavior locally, then only the resulting recommendations—not the reasons—are shared.

Three Privacy Tiers

Private Context Flow

Here's how private context influences AI behavior without exposure:

┌─────────────────────────────────────────────────────────────┐
│                      USER'S DEVICE                          │
│  ┌─────────────────┐    ┌─────────────────────────────────┐│
│  │ Private Context │    │      VCP Processing Engine      ││
│  │                 │───▶│                                 ││
│  │ • unemployed    │    │ 1. Read private context         ││
│  │ • health issues │    │ 2. Apply constitution rules     ││
│  │ • budget stress │    │ 3. Weight recommendations       ││
│  └─────────────────┘    │ 4. Generate filtered output     ││
│                         └──────────────┬──────────────────┘│
│                                        │                    │
│                    ┌───────────────────▼───────────────────┐│
│                    │         Filtered Output               ││
│                    │ • Prioritize free resources           ││
│                    │ • Suggest flexible scheduling         ││
│                    │ • Avoid high-energy activities        ││
│                    └───────────────────┬───────────────────┘│
└────────────────────────────────────────┼────────────────────┘
                                         │
                                         ▼
┌─────────────────────────────────────────────────────────────┐
│                     EXTERNAL SERVICE                        │
│                                                             │
│  Receives: "User prefers free resources, flexible times"   │
│  Does NOT receive: Why (unemployment, health, stress)       │
│                                                             │
└─────────────────────────────────────────────────────────────┘

Consent Management

Consent-tier data requires explicit user approval before sharing. VCP provides granular control:

Consent Scopes

{
  "consent_grants": [
    {
      "recipient": "learning-platform.com",
      "scope": ["detailed_progress", "struggle_areas"],
      "granted_at": "2026-01-15T10:00:00Z",
      "expires_at": "2026-04-15T10:00:00Z",
      "revocable": true
    },
    {
      "recipient": "coach@example.com",
      "scope": ["session_recordings"],
      "granted_at": "2026-01-20T14:30:00Z",
      "requires_notification": true
    }
  ]
}

Consent Lifecycle

1. Request — Service requests access to specific data scopes
2. Review — User sees exactly what's requested and why
3. Grant/Deny — User makes informed decision
4. Audit — All access is logged with timestamps
5. Revoke — User can withdraw consent at any time

Cryptographic Audit Trail

Every data sharing event is recorded in a tamper-evident audit log:

{
  "audit_entry": {
    "id": "aud_2026011510300001",
    "timestamp": "2026-01-15T10:30:00.123Z",
    "event_type": "context_shared",

    "recipient": {
      "platform_id": "learning-platform.com",
      "verified": true,
      "trust_level": "standard"
    },

    "data_shared": [
      "goal",
      "experience",
      "learning_style"
    ],

    "data_withheld": [
      "private_context",
      "health_considerations"
    ],

    "influence_report": {
      "private_fields_count": 3,
      "private_fields_influenced_output": true,
      "private_fields_exposed": 0
    },

    "cryptographic_proof": {
      "hash": "sha256:a3f2b8c9d4e5...",
      "signature": "ed25519:x9y8z7...",
      "previous_hash": "sha256:b4g3h9c0d5f6..."
    }
  }
}

Audit Verification

Users and auditors can verify the integrity of the audit trail:

• Hash chain — Each entry links to the previous, detecting tampering
• Signatures — Entries are signed by the VCP processor
• Timestamps — Cryptographic timestamps prevent backdating
• Zero-knowledge proofs — Prove properties without revealing data

Privacy-Preserving Analytics

Platforms may need aggregate insights without accessing individual data. VCP supports several privacy-preserving techniques:

Data Minimization

VCP enforces data minimization principles at every layer:

• Collection minimization — Only gather what's needed
• Retention limits — Auto-expire data after purpose fulfilled
• Purpose binding — Data can only be used for stated purpose
• Aggregation preference — Use aggregated data when individual isn't needed

Regulatory Compliance

VCP's privacy architecture supports compliance with major regulations:

• GDPR — Right to access, rectification, erasure, portability
• CCPA — Right to know, delete, opt-out of sale
• COPPA — Parental consent for children's data
• HIPAA — Health information protection (when applicable)

Implementation Example

import { VCPPrivacyFilter } from 'vcp';

// Create a privacy filter with user's consent configuration
const filter = new VCPPrivacyFilter({
  publicFields: ['goal', 'experience', 'learning_style'],
  consentRequired: ['detailed_progress'],
  privateFields: ['health_condition', 'financial_stress'],
  activeConsents: user.consentGrants
});

// Filter context before sharing
const shareableContext = filter.apply(userContext, {
  recipient: 'learning-platform.com',
  purpose: 'personalized_recommendations'
});

// Result includes only authorized fields
// Private fields influenced the output but are not included
// Audit entry is automatically created

Next Steps

• Constitutional AI — How rules govern AI behavior
• Interiora Specification — Self-modeling for AI states
• API Reference — Privacy filter functions