Access to microdata in official statistics requires balancing disclosure risk and statistical utility. This paper proposes an interpretable, open-box energy-driven framework for privacy-aware synthesis of mixed-type tabular data, combining discriminative modelling, Bayesian-network proposals, Metropolis--Hastings sampling and post-generation optimization. Explicit energy components govern record-level plausibility, proximity to training records and diversity, while target selection balances fidelity and classification utility, making these trade-offs directly inspectable. Evaluation uses an artificially constructed individual-level dataset with demographic, behavioural and health-related variables. Validation combines marginal, bivariate and conditional fidelity diagnostics, diversity measures, nearest-neighbour analyses and membership inference attacks. An arrangement of classifiers trained on reference and synthetic data is evaluated on a common reference holdout. Repeated target selection assesses stability within a fixed candidate pool. Results show a convincing trade-off between fidelity and utility of the synthetic data generated by means of the proposed framework. Membership attacks perform close to chance across repeated splits, indicating limited training-membership discrimination under the evaluated protocol. The findings support the feasibility of balancing fidelity, utility and privacy-aware objectives through explicit generation and selection criteria, without implying formal privacy guarantees.

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