Beyond Realism: A Utility-Fidelity-Privacy Framework for Trustworthy Synthetic Data

This paper presents a unified framework for evaluating synthetic data across utility, fidelity, and privacy, with the goal of improving trust and reliability in scientific applications where realism alone is not sufficient. The article defines synthetic data generation as a constraint-aware process guided by domain knowledge and introduces a structured pipeline that includes data auditing, controlled generation, and multi-criteria evaluation. The framework is validated through a Harmful Algal Bloom case study comparing statistical, deep, and quantum generative models under consistent training and evaluation settings. Results show that no single model dominates across all criteria. Statistical models best preserve correlation structure and achieve high predictive performance, deep models increase variability but reduce fidelity, and quantum models improve privacy by increasing separation from real data at the cost of accuracy. Combining real and synthetic data improves segmentation results, with the best model achieving mIoU of 0.553 and Dice of 0.668. Overall, synthetic data quality depends on trade-offs rather than a single metric. The evaluation focuses on one environmental dataset and a limited set of generative models. Results depend on data quality and chosen configurations, and fairness is not fully explored. Model selection should match the application goal: high-risk scientific tasks require strong fidelity, while privacy-sensitive settings benefit from models that reduce reconstruction risk. The framework supports structured, repeatable deployment of synthetic data pipelines from analysis, synthetic data is reliable and performance is as good as observation data. This paper introduces a multi-objective evaluation framework that integrates utility, fidelity, and privacy into a single decision process, shifting synthetic data from a realism-focused task to a governed, context-dependent system.