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:
- ✅ 363 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
- All SIL projects: Universal provenance layer enables "show your work" across the stack

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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