{
  "abstract": "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2606.16488",
  "author_total": 2,
  "authors": [
    {
      "name": "Pierpaolo Massoli"
    },
    {
      "name": "Fabio Spagnuolo"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8pefm3agntg1s.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2606.16488",
  "fulltext_url": null,
  "id": "8pefm3agntg1s",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2606.16488",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "1702|Privacy-Preserving Technologies in Data"
    ]
  ],
  "openalex": "W7164886592",
  "pdf_url": "https://arxiv.org/pdf/2606.16488",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-04",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8pefm3agntg1s.ris",
  "sources": [
    {
      "first_seen": "2026-11-06T18:21:40Z",
      "native_id": "2606.16488",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2606.16488"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "An Energy-Driven Framework for Privacy-Aware Synthetic Data Generation",
  "title_text": "An Energy-Driven Framework for Privacy-Aware Synthetic Data Generation",
  "type": "preprint",
  "updated_at": "2026-11-09T00:20:50Z",
  "url": "https://research.draconitic.com/paper/8pefm3agntg1s",
  "venue": "arXiv",
  "versions": []
}