Merkle-tree commitments for AI training datasets using SHA3-256 + ML-DSA. Prove what a model was trained on without revealing the data. Inclusion proofs are O(log n) and publicly verifiable. Survives the quantum transition for the 15-20 year shelf life of training data. 29 tests passing.
Use this model
Pull with QuantumShield
quantumshield pull quantmrkt/PQC Training Data Transparency Verify integrity
quantumshield verify quantmrkt/PQC Training Data Transparency pip install
pip install quantumshield && quantumshield pull quantmrkt/PQC Training Data Transparency PQC-Verified with ML-DSA-87
This model has a real FIPS 204 ML-DSA-87 (Dilithium5) signature from the platform signing authority. Signature chain includes 3 verification(s). Last verified 2026-03-26.
README.md
PQC Training Data Transparency
Merkle-tree commitments for AI training datasets using SHA3-256 + ML-DSA. Prove what a model was trained on without revealing the data. Inclusion proofs are O(log n) and publicly verifiable. Survives the quantum transition for the 15-20 year shelf life of training data. 29 tests passing.
Model Details
| Framework | Python |
| Parameters | N/A |
| License | Apache 2.0 |
| Signature Algorithm | ML-DSA-87 |
| Source | github |
Intended Uses
This model is registered on the QuantaMrkt quantum-safe registry. All files have been cryptographically verified using post-quantum signatures.
Quick Start
# Install the CLI pip install quantumshield # Pull the model quantumshield pull quantmrkt/PQC Training Data Transparency # Verify file integrity quantumshield verify quantmrkt/PQC Training Data Transparency
About
Merkle-tree commitments for AI training datasets using SHA3-256 + ML-DSA. Prove what a model was trained on without revealing the data. Inclusion proofs are O(log n) and publicly verifiable. Survives the quantum transition for the 15-20 year shelf life of training data. 29 tests passing.
Get this model
Pull with QuantumShield
quantumshield pull quantmrkt/PQC Training Data Transparency Verify signatures
quantumshield verify quantmrkt/PQC Training Data Transparency