Conflara

Secure Training Data Flow

TEE-Based Privacy Architecture for Enterprise AI Training

Core IP — Zero-Knowledge Data Privacy
End-to-End Secure Training Pipeline
Data Owner Proprietary datasets with contractual rights Enterprise with proprietary data Retains full ownership rights TLS 1.3 + AES-256 Encrypted Storage Data at rest, encrypted AES-256-GCM encryption at rest Keys held by data owner, not platform Attestation Request Remote Attestation TEE integrity verified Cryptographic proof that the enclave is running trusted, unmodified code before any data is released Verified Release TEE / Secure Enclave Data decrypted ONLY inside enclave Platform CANNOT see raw data Model Training Fine-tuning / LoRA Agent Evaluation Sandbox Testing Robot Certification Safety & Control Data Validation Integrity Checks Encrypted Model Output Encrypted before exit Trained model encrypted with customer's public key Customer Receives trained model Never sees raw data Gets trained model + eval metrics Zero access to training data 1 2 3 4 5 6 Encrypted Output Secure Delivery Optional: Submit ★ TCG Certification Engine Models, Agents, Robots & Data — Benchmarks, Safety, Bias Evaluation Benchmark Suite Grade Computation Badge Issuance Models, agents, robots, and data evaluated CONFLARA Certified: Bronze | Silver | Gold | Platinum Certified ★ GRADE Badge Issued 7 ZERO-KNOWLEDGE GUARANTEE Raw training data is NEVER visible to the Conflara platform, other customers, or any third party. All computations occur inside hardware-attested Trusted Execution Environments with cryptographic verification. CONFLARA CERTIFIED — Test. Certify. Trade. Post-training, models, agents, robots, and datasets can be submitted for independent testing, grading (Bronze/Silver/Gold/Platinum), and certification badge issuance. 🔒 ✔
Data Visibility Matrix
Role CAN See CANNOT See
Platform Operator
Conflara infrastructure		 ✓ Job metadata & status
✓ Billing & usage metrics
✓ Encrypted data checksums
✓ Attestation reports 		✗ Raw training data
✗ Model weights during training
✗ Decrypted gradients
✗ Customer model internals
Data Owner
Dataset provider		 ✓ TEE attestation report
✓ Training metrics (loss, accuracy)
✓ Usage logs for their data
✓ Revenue & licensing info 		✗ Other providers' datasets
✗ Customer identity (if anonymized)
✗ Final model weights
✗ Other training job details
Customer
Model buyer		 ✓ Trained model (encrypted delivery)
✓ Evaluation metrics & benchmarks
✓ Training configuration used
✓ Attestation certificate 		✗ Raw training data
✗ Data owner identity (if anonymized)
✗ Other customers' models
✗ Platform internal metrics
Secure Computation Architecture Patterns
A

TEE-Based Training

NVIDIA H100 Confidential Computing • AWS Nitro Enclaves • Intel SGX
Data Encrypted TEE Decrypt Train Re-encrypt Model Output

All training data enters a hardware-isolated enclave. The CPU/GPU processes data in encrypted memory that is inaccessible to the host OS, hypervisor, or platform operator. Maximum data protection with the simplest trust model.

B

Confidential Federated Learning

Flower Framework • Differential Privacy • Secure Aggregation
Org A Org B gradients gradients Secure Aggregator TEE-hosted Global Model

Training data never leaves the data owner's premises. Only encrypted model gradients are shared with a secure aggregator. Enables multi-organization training while maintaining complete data locality and privacy compliance.

C

Secure Multi-Party Computation

Secret Sharing • Garbled Circuits • Homomorphic Encryption
P1 P2 shares shares MPC Protocol Compute on encrypted shares No party sees full data Result Only

Data is split into cryptographic "secret shares" distributed across multiple computing nodes. No single node ever holds the complete data. Computations produce correct results while keeping inputs hidden through mathematical guarantees.

D

Secure Robot Certification

Hardware-in-the-Loop Testing • Sandbox Simulation • Safety Compliance
Robot / Agent Sandbox Isolated TEE Env Safety Test ★ Cert

Physical AI robots and autonomous agents are tested in isolated sandboxes before certification. Safety boundaries, control compliance, and real-world behavior are verified through hardware-in-the-loop simulation inside TEE-protected environments.

CONFLARA Secure Training Architecture v1.0 — Confidential — Investor Materials