DISCO-TAB: A Hierarchical Reinforcement Learning Framework for Privacy-Preserving Synthesis of Complex Clinical Data

Arshia Ilaty; Hossein Shirazi; Amir Rahmani; Hajar Homayouni

cs.LGApr 1, 2026

DISCO-TAB: A Hierarchical Reinforcement Learning Framework for Privacy-Preserving Synthesis of Complex Clinical Data

Authors: Arshia Ilaty, Hossein Shirazi, Amir Rahmani, Hajar Homayouni

Organizations: Computational Science Research Center, San Diego State University, San Diego, CA 92182 USA. · School of Nursing and Department of Computer Science, University of California, Irvine, CA 92697 USA. · Fowler College of Business, San Diego State University, San Diego, CA 92182 USA. · Department of Computer Science, San Diego State University, San Diego, CA 92182 USA.

Abstract

The development of robust clinical decision support systems is frequently impeded by the scarcity of high-fidelity, privacy-preserving biomedical data. While Generative Large Language Models (LLMs) offer a promising avenue for synthetic data generation, they often struggle to capture the complex, non-linear dependencies and severe class imbalances inherent in Electronic Health Records (EHR), leading to statistically plausible but clinically invalid records. To bridge this gap, we introduce DISCO-TAB (DIScriminator-guided COntrol for TABular synthesis), a novel framework that orchestrates a fine-tuned LLM with a multi-objective discriminator system optimized via Reinforcement Learning. Unlike prior methods relying on scalar feedback, DISCO-TAB evaluates synthesis at four granularities, token, sentence, feature, and row, while integrating Automated Constraint Discovery and Inverse-Frequency Reward Shaping to autonomously preserve latent medical logic and resolve minority-class collapse. We rigorously validate our framework across diverse benchmarks, including high-dimensional, small-sample medical datasets (e.g., Heart Failure, Parkinson's). Our results demonstrate that hierarchical feedback yields state-of-the-art performance, achieving up to 38.2% improvement in downstream clinical classifier utility compared to GAN and Diffusion baselines, while ensuring exceptional statistical fidelity (JSD < 0.01) and robust resistance to membership inference attacks. This work establishes a new standard for generating trustworthy, utility-preserving synthetic tabular data for sensitive healthcare applications.

Figures & tables

Fig. 1: The DISCO-TAB framework for tabular data synthesis. (A) Automated Constraint Discovery: The system analyzes the real dataset ( $\mathcal{D}_{real}$ ) to compute a global correlation matrix and identify statistically significant feature dependencies (e.g., Age vs. Comorbidities), which are used to initialize the feature-level discriminator ( $D_{feat}$ ). (B) Hierarchical Discriminators: A multi-objective discriminator ensemble evaluates generated records ( $S$ ) at four semantic granularities: token-level (syntactic validity), sentence-level (local semantic plausibility), feature-level (dependency consistency), and row-level (global coherence). (C) Inverse Frequency Reward Shaping (IFRS): During reinforcement learning, the PPO reward is scaled based on the rarity of the target class associated with the generated record, mitigating mode collapse in imbalanced datasets.

Dataset	CTGAN	TAEGAN	TabDDPM	TabSyn	RealTabFormer	MostlyAI	TAGAL	DISCO-TAB	$\Delta$
Heart Failure	0.778	0.655	0.665	0.877	0.548	0.598	0.839	1.000	+0.123
Breast Cancer	0.549	0.693	0.689	0.934	0.495	0.498	0.990	0.994	+0.004
Liver Disorders	0.496	0.576	0.133	0.510	0.528	0.574	0.608	0.990	+0.382
Parkinsons	0.594	0.595	0.637	0.610	0.637	0.637	0.886	0.966	+0.080
Obesity	0.105	0.215	0.577	0.902	0.250	0.584	0.790	0.929	+0.027
Diabetes	0.881	0.875	0.840	0.950	0.968	0.969	0.882	0.865	-0.104

TABLE I: Downstream ML utility (F1-score) under the Train-on-Synthetic, Test-on-Real (TSTR) protocol.

\Delta

denotes the absolute percentage point differences between DISCO-TAB and the strongest baseline per dataset (positive indicates DISCO-TAB superiority; negative indicates a stronger baseline). The first six datasets are medical benchmarks, while the last three (German Credit, Bank Marketing, Census Income) are non-medical datasets included to evaluate cross-domain generalizability.

	DISCO-TAB Metrics					Baseline KS Comparison (Lower is Better)
Dataset	JS $\downarrow$	KL $\downarrow$	Hell. $\downarrow$	KS $\downarrow$	Pearson $\uparrow$	MostlyAI	CTGAN	TAEGAN	RealTab	TabDDPM	TabSyn	TAGAL
Heart Failure	0.003	0.017	0.044	0.022	0.968	0.085	0.147	0.212	0.125	0.098	0.045	0.039
Breast Cancer	0.011	0.090	0.095	0.025	0.992	0.038	0.339	0.268	0.150	0.210	0.032	0.045
Liver Disorders	0.017	0.147	0.094	0.017	0.978	0.063	0.337	0.416	0.220	0.350	0.085	0.095
Parkinsons	0.005	0.028	0.067	0.034	0.999	0.066	0.315	0.374	0.110	0.095	0.052	0.062
Obesity	0.008	0.044	0.060	0.023	0.986	0.184	0.301	0.274	0.250	0.045	0.035	0.080

TABLE II: Unified statistical fidelity comparison. Columns under DISCO-TAB report our performance across five metrics. The right side compares the KS Statistic (lower is better) against baselines. DISCO-TAB achieves consistently low KS divergence across all medical datasets, consistently outperforming Diffusion models (TabDDPM) on complex data. Pearson correlation is reported exclusively for continuous feature pairs. In addition, distributional similarity metrics between real and synthetic data. KL divergence (

D_{KL}(P\parallel Q)

) is computed with additive smoothing (

\epsilon=10^{-8}

) to ensure numerical stability. Jensen–Shannon Divergence (JSD) is additionally reported as a symmetric and bounded alternative.

Dataset	Metric	TabDDPM	MostlyAI	DISCO-TAB
Heart Failure	Utility (F1)	0.665	0.598	1.000
Heart Failure	Privacy (DCR)	4830.6	895.0	1.27
Obesity	Utility (F1)	0.577	0.584	0.929
Obesity	Privacy (DCR)	6.8	1.9	1.5
German Credit	Utility (F1)	0.592	0.626	0.925
German Credit	Privacy (DCR)	3.3	2.1	1.9

TABLE III: Privacy-Utility Trade-off across diverse scales. We report Utility (F1-Score) and Privacy (DCR). TabDDPM consistently achieves extreme DCR values due to diffusion noise, but often at the cost of utility (e.g., on Obesity). DISCO-TAB maintains a pragmatic safe zone (

DCR>0

) while strictly prioritizing the clinical fidelity required for high F1 scores.

Dataset	CTGAN	TAEGAN	TabDDPM	TabSyn	MostlyAI	Real.	DISCO-Tab
Heart Fail.	2.2	2.5	10.1	8.5	4.2	5.1	4.8
Breast Can.	2.3	2.6	10.4	8.7	4.3	5.2	4.9
Liver Dis.	2.1	2.4	9.8	8.3	4.1	4.9	4.6
Parkinsons	2.1	2.4	9.8	8.4	4.1	5.0	4.7
Obesity	2.4	2.7	10.8	8.9	4.4	5.3	5.0
Diabetes	2.4	2.8	11.4	9.2	4.7	5.5	5.2

TABLE IV: Computational Efficiency: Peak GPU Memory Usage (GB). Datasets are grouped by scale (Small vs. Large). DISCO-TAB remains memory-efficient (

<6

GB), unlike Diffusion models.

	CTGAN	TAEGAN	TabDDPM	TabSyn	RealTabFormer	TAGAL	MostlyAI	DISCO-TAB
Num. JSD	0.145	0.130	0.098	0.085	0.092	0.140	0.035	0.038
Cat. JSD	0.210	0.195	0.155	0.120	0.048	0.030	0.042	0.015

TABLE V: Feature-Type Fidelity Analysis (Mean JSD

\downarrow

). DISCO-TAB bridges the gap between numerical and categorical precision.

Model	Overall FAITH	Factuality	Alignment	Integrity	Tracking	Behavioral Profile
MostlyAI	0.9747	0.9993	0.9026	0.9970	0.9999	Balanced Commercial Baseline
Taegan	0.9572	1.0000	0.8618	1.0000	0.9671	High Constraint / Lower Correlation
Tab-DDPM	0.6773	0.1976	0.8722	1.0000	0.6392	Privacy via Noise (High Integrity, Low Factuality)
DISCO-TAB	0.7703	0.8750	0.9839	0.2303	0.9920	Precision via Manifold Adherence

TABLE VI: FAITH Framework Decomposition. DISCO-TAB dominates in Alignment (

0.9839

), proving it captures the physics of clinical data better than any baseline. However, its low Integrity score reveals a fundamental trade-off: unlike diffusion models ( Tab-DDPM ) that achieve high Integrity by generating noisy samples far from the manifold, DISCO-TAB adheres strictly to the clinical distribution, prioritizing precision over diversity.

Category	Configuration	Relative Perf.	Time (min)
System	Full DISCO-TAB (PPO)	100%	26.3
	Variant: DPO	99.2%	24.8
	No Hierarchy (Row Only)	86.9%	16.5
	Minimal Baseline (No RL)	63.0%	8.7
Discriminators	All Four Levels	100%	26.3
	w/o Token Disc	97.2%	24.1

TABLE VII: Comprehensive Ablation Study Results. Aggregate performance is computed as the unweighted mean TSTR F1-score across all datasets. All datasets are weighted equally to avoid dominance by large benchmarks.

Figure 9

Synthetic Tabular Data Generation

Synthetic tabular data generation creates artificial records that preserve useful feature relationships while limiting exposure of sensitive data.

See all 98 papers

PSyGenTAB: A Privacy-Preserving Framework for Synthetic Clinical Tabular Data Generation via Constrained Optimization
Jun 16, 2026Arshia Ilaty, Hossein Shirazi, Manasi Chitale +5Synthetic Tabular Data Generation Tabular Data Synthesis
San Diego State University, San Diego, CA 92182 USA. · University of California, Irvine, CA 92697 USA.
ReTabSyn: Realistic Tabular Data Synthesis via Reinforcement Learning
Mar 11, 2026Xiaofeng Lin, Seungbae Kim, Zhuoya Li +3Synthetic Tabular Data Generation Tabular Data Synthesis
University of California, Los Angeles, USA · University of South Florida, USA · Cisco, USA +1
Memisis: Orchestrating and Evaluating Synthetic Data for Tabular Health Datasets
May 18, 2026Nitish Nagesh, Pengbao Zhou, Atchuth Naveen Chilaparasetti +8Healthcare Synthetic Tabular Data Generation
University of California, Irvine, CA, USA · PathAI, USA

Jun 16, 2026J/K move · Enter open · S save

PSyGenTAB: A Privacy-Preserving Framework for Synthetic Clinical Tabular Data Generation via Constrained Optimization

Arshia Ilaty, Hossein Shirazi, Manasi Chitale, Kedar Hegde+4

San Diego State University, San Diego, CA 92182 USA. · University of California, Irvine, CA 92697 USA.

Synthetic Tabular Data Generation Tabular Data Synthesis Privacy-Utility Tradeoff

Open paper AskPDF arXiv

The development of medical AI is constrained by limited access to high-quality clinical data due to institutional silos and strict privacy regulations such as HIPAA and GDPR. Synthetic data generation offers a potential solution, but existing methods lack principled mechanisms to explicitly manage the privacy-utility trade-off, often degrading clinically meaningful patterns or risking patient re-identification. We present PSyGenTAB, a privacy-preserving generative framework that formulates synthetic healthcare data generation as a constrained optimization problem solved using the Augmented Lagrangian Method. By embedding configurable privacy constraints directly into model training, PSyGenTAB enforces minimum privacy thresholds while maximizing clinical data utility. Across multiple clinically motivated benchmarks, PSyGenTAB preserves inter-feature clinical relationships and minority-class diagnostic patterns essential for reliable health AI. Downstream evaluation using Train-on-Synthetic, Test-on-Real and Train-on-Real, Test-on-Synthetic protocols shows that models trained on synthetic data achieve performance comparable to those trained on real patient records. Privacy auditing further demonstrates reduced exact record reproduction and strong resilience to membership inference attacks. These results establish PSyGenTAB as a principled framework for balancing privacy protection and clinical utility in synthetic healthcare data, supporting secure cross-institutional AI development.

Abstract

Synthetic Tabular Data Generation

Explore similar work

PSyGenTAB: A Privacy-Preserving Framework for Synthetic Clinical Tabular Data Generation via Constrained Optimization

ReTabSyn: Realistic Tabular Data Synthesis via Reinforcement Learning

Memisis: Orchestrating and Evaluating Synthetic Data for Tabular Health Datasets