Synthetic Data Vault

The Synthetic Data Vault operates within a fully air-gapped or tightly firewalled network segment, ensuring that sensitive source data never traverses external networks. The architecture enforces a unidirectional flow: real data enters the vault for model training, but only synthetic data is permitted to exit.

• Network Segmentation: Dedicated VLANs with no default route to the internet.
• Egress Filtering: Deep packet inspection blocks any outbound traffic containing real data signatures.
• Physical Disconnection: Supports true air-gap deployment for defense and intelligence applications.

The vault leverages Trusted Execution Environments (TEEs) such as Intel SGX or AMD SEV to encrypt data in use. During the synthesis process, real data is decrypted only within the CPU's encrypted enclave, making it invisible to the host operating system, hypervisor, and cloud administrators.

• Memory Encryption: Protects against cold-boot attacks and privileged user access.
• Remote Attestation: Cryptographically verifies the integrity of the vault software stack before execution.
• Secure Key Management: Integrated with on-premises Hardware Security Modules (HSMs).

Unlike flat-file generators, the vault preserves referential integrity across complex relational databases. It models primary key-foreign key relationships, ensuring that synthetic child tables correctly reference synthetic parent tables without generating orphaned records.

• Graph-Based Modeling: Captures inter-table dependencies as a directed acyclic graph.
• Sequential Generation: Parent tables are synthesized before dependent child tables.
• Consistent Keys: Synthetic foreign keys map directly to valid synthetic primary keys.

The vault abstracts the synthesis backend, allowing data scientists to select the optimal algorithm for their data structure without changing the privacy or governance layer. Supported engines include CTGAN for tabular data, TVAE for continuous distributions, and Gaussian Copula for statistical baselines.

• Algorithm Agnosticism: Swap between GANs, VAEs, and copulas via a unified API.
• Custom Model Injection: Register proprietary generative models as new synthesis backends.
• Automated Selection: Metadata analysis recommends the best engine for the schema.

The vault implements a Privacy Budget Manager that tracks cumulative epsilon expenditure when Differentially Private Stochastic Gradient Descent (DP-SGD) is activated. Administrators set a global epsilon ceiling, and the system halts generation if the budget is exhausted, preventing accidental privacy erosion.

• Real-Time Tracking: Monitors privacy loss with each training epoch.
• Hard Limits: Enforces organizational privacy policy at the algorithmic level.
• Audit Logging: Immutable logs record every privacy budget deduction for compliance.

Every action within the vault—from data ingestion and model training to synthetic data export—is logged to a tamper-proof registry. Cryptographic hashing chains the logs, ensuring that compliance officers can verify the complete lineage of a synthetic dataset without trusting a central database administrator.

• Cryptographic Chaining: Each log entry includes the hash of the previous entry.
• Non-Repudiation: Digital signatures identify the specific user or service account that initiated each action.
• Compliance Reporting: Automated generation of audit reports for GDPR and HIPAA.