In recent years, the massive use of various data sources and access methods has led to violations of citizens’ privacy. Driven by the fears of exposure and breaches, the provision of data to interested parties such as government agencies, and security companies has become increasingly limited. In response to these concerns, we have explored best practices and advanced models for processing and anonymizing citizen data tables. Traditional techniques fail to balance data utility with privacy. This paper proposes a two-tier privacy framework: a) a Conditional Tabular Generative Adversarial Networks (CTGAN) model based on a deep learning data synthesizer, which maintain statistical fidelity while mitigating the risks of re-identification, and b) an API-mediated access system that allows interested authorized parties to dynamically query synthetic data and perform statistical analyses without compromising data security. Based on our analysis, the synthetic data preserves approximately 92% of the utility of the original data, meaning the statistical structure (SD metrics) is preserved. Also, the re-identification risk has been reduced to nearly 0% - representing nearly 100% reduction in re-identification risk from the original data (with 1.8% success rate in penetration test). The API layer further enforces granular control, ensuring that synthetic data is only generated for approved queries. To demonstrate the system’s capabilities, we conducted hypothesis tests, such as gender-based income disparity via API calls, showing how researchers can extract insights safely. The system also reduces data preparation time by 60% compared to manual aggregation methods. Case studies use datasets confirm compliance with both GDPR and INSTAT standards.