Innovations

Genesisgraph

Proving how things were made—without revealing how you made them

The Problem

The Certification vs. IP Protection Dilemma

Industries face an impossible choice:

  • Regulators demand transparency: "Show us your AI training data, model parameters, and decision process"
  • Businesses need IP protection: "We can't reveal our proprietary pipeline or competitive advantages"
  • Current solution: Choose between compliance and competitive moat

Real-world blockers:
- AI/ML: FDA wants to see model training process → Can't reveal proprietary architectures
- Manufacturing: ISO 9001 requires process documentation → Can't expose trade secret recipes
- Research: Journals require reproducibility → Can't share sensitive experimental data
- Healthcare: HIPAA compliance requires audit trails → Can't reveal patient information

Result: Regulated industries avoid adoption of advanced techniques because verification requires total transparency.

The Innovation

Three-level selective disclosure (A/B/C) solves the "certification vs IP" dilemma:

GenesisGraph enables cryptographically verifiable provenance where you choose exactly what to reveal:

Level A: Full Disclosure (Open Science)

Use when: Open source projects, public research, transparency required

# Show everything: full pipeline, parameters, intermediate results
operations:
  - id: train
    tool: pytorch
    parameters:
      learning_rate: 0.001
      batch_size: 32
      epochs: 100
    inputs: [training_data]
    outputs: [model_v1]

Level B: Partial Envelope (Verified Privacy)

Use when: Prove constraints without exact values

# Reveal that constraints were met, hide exact parameters
operations:
  - id: train
    tool: pytorch
    sealed_parameters:
      # Hash of actual parameters
      digest: "sha256:abc123..."
      # Provable constraints
      constraints:
        - "learning_rate < 0.01"
        - "batch_size >= 16"
        - "epochs >= 50"
    inputs: [training_data]  # Can seal these too
    outputs: [model_v1]

Level C: Sealed Subgraph (Zero-Knowledge)

Use when: Hide entire pipeline segments, prove policy compliance only

# Replace proprietary pipeline with Merkle root commitment
sealed_subgraph:
  root_hash: "sha256:def456..."
  inputs: [raw_data]      # Show what went in
  outputs: [final_model]  # Show what came out
  policies:
    - claim: "FDA 21 CFR Part 11 compliant"
      signature: "..."
    - claim: "No PII in training data"
      signature: "..."

The magic: Cryptographic commitments (Merkle trees, hash chains) enable proving properties without revealing values.

Quick Example: AI Model with Trade Secret Protection

Scenario: Train AI model with proprietary architecture, prove FDA compliance, protect IP.

from genesisgraph import GenesisGraph, Entity, Operation, SealedSubgraph

gg = GenesisGraph(spec_version="0.3.0")

# Level A: Public data preprocessing (show everything)
gg.add_operation(Operation(
    id="preprocess",
    tool="pandas",
    parameters={"method": "normalize", "axis": 0},
    inputs=["raw_data"],
    outputs=["clean_data"]
))

# Level C: Proprietary training pipeline (seal completely)
gg.add_sealed_subgraph(SealedSubgraph(
    root_hash="sha256:abc123...",
    inputs=["clean_data"],
    outputs=["model_v1"],
    policies=[
        {"claim": "FDA 21 CFR Part 11 compliant", "signature": "..."},
        {"claim": "No patient PII in training data", "signature": "..."},
        {"claim": "Model accuracy > 95% on validation set", "signature": "..."}
    ]
))

# Level A: Public model evaluation (show everything)
gg.add_operation(Operation(
    id="evaluate",
    tool="sklearn",
    parameters={"metrics": ["accuracy", "f1"]},
    inputs=["model_v1", "test_data"],
    outputs=["evaluation_report"]
))

# Export provenance graph
gg.save_yaml("ai_pipeline.gg.yaml")

What regulators see:
- ✅ Complete audit trail (inputs → sealed training → outputs → evaluation)
- ✅ Cryptographic proof of policy compliance (FDA 21 CFR Part 11)
- ✅ Verifiable integrity (Merkle tree commitments)

What competitors don't see:
- ❌ Proprietary training architecture
- ❌ Hyperparameter optimization strategy
- ❌ Custom loss functions
- ❌ Data augmentation techniques

Both verified with cryptographic certainty.

Status & Adoption

Current Version: v0.3.0 (Production-Ready)

Production Metrics:
- ✅ 320 comprehensive tests across all modules
- ✅ 76% overall test coverage (up from 71% in v0.2)
- ✅ 98% SD-JWT coverage - IETF standard selective disclosure
- ✅ 99% BBS+ coverage - Zero-knowledge credential proofs
- ✅ 97% ZKP templates coverage - Range proofs, membership proofs
- ✅ 90% DID support - Multi-method decentralized identity (did:key, did:web, did:ion, did:ethr)

Novel Research Contributions:

  1. Three-Level Selective Disclosure Model (A/B/C)
    - Level A: Full transparency for open science
    - Level B: Constraint proofs without exact values (SD-JWT)
    - Level C: Sealed subgraphs with policy assertions (Merkle commitments)
    - Industry first: Unified framework spanning full transparency → zero-knowledge

  2. Merkle Tree Provenance Commitments
    - Hash-only lineage for proprietary pipeline segments
    - Selective exposure of input/output digests
    - Optional inclusion proofs without revealing full tree
    - RFC 6962 transparency log integration

  3. Industry-Specific Profile Validators
    - gg-ai-basic-v1: AI/ML pipeline validation (FDA 21 CFR Part 11 compliance)
    - gg-cam-v1: Computer-aided manufacturing (ISO 9001:2015 compliance)
    - Automated compliance checking for regulated industries

  4. Cryptographic Privacy Features
    - SD-JWT (Selective Disclosure JWT) for claim-level privacy
    - BBS+ signatures with unlinkable selective disclosure
    - Holder binding prevents credential replay attacks
    - Predicate proofs (e.g., "age > 21" without revealing exact age)

What This Unlocks:
- Regulated AI adoption - FDA/EMA approval without IP disclosure
- Manufacturing compliance - ISO 9001 certification with trade secret protection
- Research reproducibility - Verify methods without sharing sensitive data
- Healthcare audit trails - HIPAA compliance with patient privacy

SDKs Available:
- Python SDK: pip install genesisgraph (93% coverage)
- JavaScript/TypeScript SDK: npm install @genesisgraph/sdk
- Full builder API, validation, DID resolution, signature verification

v1.0 Release Timeline: Active development
- Enterprise adoption (Fortune 500 pilots)
- Standards body submission (W3C, IETF)
- Blockchain integration (Ethereum, Hyperledger)

Technical Deep Dive

Full Documentation:
- GenesisGraph GitHub Repository
- Complete Specification
- Disclosure Levels Guide - A/B/C model explained
- Selective Disclosure Cryptography - SD-JWT, BBS+, ZKP
- Profile Validators - Industry compliance

Example Gallery:
- AI/ML Pipelines
- Manufacturing Workflows
- Research Reproducibility

Getting Started:

pip install genesisgraph
genesisgraph validate workflow.gg.yaml --verify-profile

Part of SIL's Semantic OS Vision

GenesisGraph's Role in the 7-Layer Semantic OS:

  • Layer 2 (Structures): Provenance data structures
  • Directed acyclic graphs (DAGs) for process lineage
  • Merkle trees for cryptographic commitments

  • Hash chains for temporal ordering

  • Layer 3 (Composition): Provenance graph composition

  • Graphs compose: Subgraphs seal and embed in larger workflows
  • Selective disclosure composes: Mix A/B/C levels in single graph

  • Policy assertions compose: Multiple compliance claims per operation

  • Cross-Cutting Concern: Provenance infrastructure across all layers

  • Enables verifiable transformations at every layer (primitives → intelligence)
  • Universal audit trail for semantic operations
  • Foundation for trustworthy AI and autonomous systems

Composes With:
- Pantheon (Layer 3): Provenance-aware IR - Track semantic graph transformations
- Morphogen (Layer 1/4): Deterministic execution - Provenance for computational workflows
- Agent Ether (Layer 6): Multi-agent systems - Verifiable agent actions and decisions
- Semantic Trust Fabric: Trust assertions stored as typed graph edges with full provenance
- All SIL projects: Universal provenance layer enables "show your work" across the stack

Trust Layer Integration

GenesisGraph serves as the storage layer for the Trust Assertion Protocol (TAP). Trust Assertions become typed edges in the graph:

# Trust assertion as GenesisGraph edge
edge:
  type: trust_assertion
  issuer: "did:key:z6Mk..."
  subject: "did:key:z6Mn..."
  claim:
    type: has-capability
    value: distributed-systems
    level: expert
  provenance:
    graph_node: "gg:node:12345"
    timestamp: "2025-02-15T12:00:00Z"
    chain: ["gg:node:12344", "gg:node:12343"]
  proof:
    type: zk-snark
    statement: ">50 commits to distributed systems repos"

What this enables:

  1. Trust with Provenance — Every trust assertion has full lineage
  2. Multi-hop Trust Reasoning — Query "who trusts whom, and why?"
  3. Contextual Trust — Filter trust by domain, scope, validity period
  4. ZK-blinded Trust — Prove trust relationships without revealing details
  5. Agent Capability Verification — Verify agent claims before delegation

Integration Points:

Component GenesisGraph Role
Trust Assertion Protocol (TAP) Assertions stored as graph edges
Agent Ether Agent capabilities verified via graph queries
Semantic Passports Bundles of assertions with provenance chains

See: Trust Assertion Protocol for the full TAP specification.

Architectural Principle: Verification Without Revelation

GenesisGraph proves that privacy and verifiability are not opposites—they're composable. When you can cryptographically commit to properties without revealing values, compliance and competition can coexist.

The Innovation:
Most provenance systems are binary (public or private). GenesisGraph introduces a spectrum of disclosure (A/B/C) that adapts to context:
- Open science: Full transparency builds trust
- Regulated industries: Prove compliance, protect IP
- Competitive markets: Selective disclosure enables verification without revelation

This solves adoption blockers in regulated industries where existing provenance standards force impossible choices.

Impact: Real-World Adoption Paths

Before GenesisGraph:
- Prove FDA compliance → Reveal proprietary AI architecture → Lose competitive advantage
- ISO 9001 certification → Expose manufacturing trade secrets → Competitors clone process
- Research reproducibility → Share sensitive patient data → HIPAA violation

With GenesisGraph:
- Seal proprietary pipeline segments (Level C)
- Prove policy compliance cryptographically (Merkle commitments, signatures)
- Regulators verify integrity, competitors see only commitments
- First technology that enables verification without revelation at scale

Use Cases Enabled:

  1. AI/ML Pipelines
    - FDA/EMA approval for medical AI without exposing training process
    - Model cards with verifiable provenance (training data lineage, bias testing)
    - Responsible AI compliance (fairness, transparency, accountability)

  2. Manufacturing & Supply Chain
    - ISO 9001 certification with trade secret protection
    - Quality control audit trails (machine calibration, tolerance tracking)
    - Supplier verification without revealing proprietary recipes

  3. Scientific Research
    - Reproducible research with sensitive data protection
    - Peer review with selective disclosure (methods public, data sealed)
    - Clinical trials with patient privacy (HIPAA compliance)

  4. Enterprise IT
    - Software supply chain security (SBOM with selective disclosure)
    - DevOps audit trails (deployment provenance, compliance checks)
    - Zero-trust architectures with verifiable process lineage

Adoption Metrics (v1.0 Goals):
- 10+ Fortune 500 pilots across AI, manufacturing, healthcare
- 2+ standards body submissions (W3C, IETF)
- 100+ open source projects integrating GenesisGraph SDKs

Version: 0.3.0 → 1.0 (Active Development)
License: Apache 2.0
Status: Production-ready with enterprise adoption path

Learn More:
- GitHub Repository
- 5-Minute Quickstart
- Vision & Roadmap

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