Structured synthetic data is presented as a maturing technology for privacy preservation that facilitates compliance with data protection regulations. The difficulty of defining when such data can be considered anonymous under existing legal frameworks is highlighted, given the absence of a universal standard, even as emerging best practices begin to provide guidance for privacy compliance. A comprehensive analysis is provided of the relevant legal and regulatory context, empirical methods for privacy risk evaluation, and adversarial threat modeling approaches. Privacy risk metrics and technical mitigation techniques are examined, and governance considerations for enterprise data management and compliance are addressed. Finally, the need for industry standard-setting initiatives is underscored, and a recommendation is made to pursue formal standards for privacy-preserving data synthesis.
- Status
- Published White Paper
- History
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- Published:
- 2025-10-09
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