# Empirical Evaluation of Structured Synthetic Data Privacy Metrics: Novel experimental framework

arXiv: https://arxiv.org/abs/2512.16284 · HTML: https://mlsanity.com/paper/2512.16284/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2025.

## Built on by (most cited first, 1 of 1)

- [ReMIA: a Powerful and Efficient Alternative to Membership Inference Attacks against Synthetic Data Generators](https://mlsanity.com/paper/2605.14686/) (2026)

## Abstract (arXiv)

Synthetic data generation is gaining traction as a privacy enhancing technology (PET). When properly generated, synthetic data preserve the analytic utility of real data while avoiding the retention of information that would allow the identification of specific individuals. However, the concept of data privacy remains elusive, making it challenging for practitioners to evaluate and benchmark the degree of privacy protection offered by synthetic data. In this paper, we propose a framework to empirically assess the efficacy of tabular synthetic data privacy quantification methods through controlled, deliberate risk insertion. To demonstrate this framework, we survey existing approaches to synthetic data privacy quantification and the related legal theory. We then apply the framework to the main privacy quantification methods with no-box threat models on publicly available datasets.