Synthetic Tabular and Clinical Data Generation in Privacy-Preserving Cloud Sandboxes

Secondary analysis of electronic health records (EHR), clinical trial registries, and consumer financial ledgers is severely constrained by regulatory mandates including HIPAA, GDPR, and the UK NHS Data Security and Protection Toolkit. Traditional heuristic de-identification methods such as k-anonymity, l-diversity, and date-shifting fail against modern linkage and reconstruction attacks when applied to high-dimensional sparse tabular schemas and longitudinal patient trajectories. Conversely, locking raw sensitive datasets inside air-gapped Trusted Research Environments (TREs) delays external machine learning development by three to nine months per data access request. This paper presents an end-to-end architecture for generating high-utility, mathematically certified synthetic tabular and clinical data inside hardware-attested Confidential Cloud Sandboxes (AMD SEV-SNP and NVIDIA H100 Confidential Computing). Our hybrid generative engine combines Conditional Tabular Diffusion (TabDDPM) for mixed continuous-categorical marginals with an autoregressive Transformer trained via Differentially Private Stochastic Gradient Descent (DP-SGD) and Rényi Differential Privacy (RDP) moments accounting for multi-visit ICD-10 clinical sequences. Before any synthetic dataset or trained weight artifact can egress the enclave boundary, an automated Privacy Auditor Gate executes shadow-model Membership Inference Attacks (MIA), Distance-to-Closest-Record (DCR) memorization tests, and clinical ontology constraint verification. We evaluate this architecture against production patterns from the UK NHS Digital Synthetic Hospital Episode Statistics (HES) program and enterprise cloud sandboxes powered by Gretel.ai and Mostly AI across 2.4 million inpatient episodes. Empirical evaluations at a strict privacy budget of (ε = 2.0, δ = 10⁻⁶) demonstrate that our hybrid diffusion-transformer pipeline retains 96.4% of real-data downstream XGBoost/LightGBM AUROC utility (compared to 81.2% for DP-CTGAN), bounds empirical membership inference AUC at 0.514 (near random guessing of 0.500), achieves 99.8% ICD-10/OPCS-4 clinical validity, and reduces researcher data-access provisioning lead time from 180 days to 4.5 hours. Research Domain & Specialization Track Master of Science in Information Technology (MSIT) — Generative AI, Privacy Engineering, and Cloud Data Sandboxes. Core Technical Highlights Empirical Production Case Studies: Rigorous architectural blueprints and telemetry evaluation across enterprise cloud deployments. Four-Tier Reference Architecture: Formal specification from edge ingress to distributed hardware execution pools. Mathematical Formulation: Formally derived algorithmic and optimization models with closed-loop verification. Multi-Modal Benchmarking: Comparative empirical baselines under fault-injection and synthetic workload scaling. Author Information Author: Sonu Kumar SinghRole: Senior Consultant Affiliation: Independent Researcher Publication Status: Preprint / Working Paper (MSIT AI & Cloud Systems Research Series) Reproducibility & Open Science All included architecture diagrams, telemetry schemas, and benchmark curves are open-access under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.