# SecretFan: Synthesizing Realistic Data without Breaking Privacy

arXiv: https://arxiv.org/abs/2602.05833 · HTML: https://mlsanity.com/paper/2602.05833/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 37 references that are in the map.
- First posted on arXiv 5 Feb 2026.

## Builds on (most cited first, 9 of 9)

- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [CTAB-GAN: Effective Table Data Synthesizing](https://mlsanity.com/paper/2102.08369/) (2021)
- [Fairness Testing: A Comprehensive Survey and Analysis of Trends](https://mlsanity.com/paper/2207.10223/) (2022)
- [Scaling While Privacy Preserving: A Comprehensive Synthetic Tabular Data Generation and Evaluation in Learning Analytics](https://mlsanity.com/paper/2401.06883/) (2024)
- [An Empirical Study on the Membership Inference Attack against Tabular Data Synthesis Models](https://mlsanity.com/paper/2208.08114/) (2022)
- [Synthetic Tabular Data Generation: A Comparative Survey for Modern Techniques](https://mlsanity.com/paper/2507.11590/) (2025)
- [Why Data Anonymization Has Not Taken Off](https://mlsanity.com/paper/2509.10165/) (2025)
- [Synthesizing Precise Protocol Specs from Natural Language for Effective Test Generation](https://mlsanity.com/paper/2511.17977/) (2025)
- [Inferring Input Grammars from Code with Symbolic Parsing](https://mlsanity.com/paper/2503.08486/) (2025)

## Abstract (arXiv)

There is a need for synthetic training and test datasets that replicate statistical distributions of original datasets without compromising their confidentiality. A lot of research has been done in leveraging Generative Adversarial Networks (GANs) for synthetic data generation, however the resulting models are either not accurate enough or are still vulnerable to membership inference attacks (MIA) or dataset reconstruction attacks since the original data has been leveraged in the training process. In this paper, we frame synthetic data generation as a guided test generation, or search-based testing problem rather than a purely generative modeling task. Ours is a search-based, adequacy-guided input generation technique inspired by GANs, with a generation step and a discrimination step; as in GAN, discrimination uses a discriminator model trained on the date, but instead of using models also for generation, we use a fuzzer. This way, the original (private) data is only indirectly leveraged in the generation process, and by evolving samples and determining "good samples" with the discriminator, we can generate privacy-preserving data that follows the same statistical distributions as the original dataset, leading to a similar utility as the original data. We evaluated our approach on eight datasets that have been used to evaluate the state-of-the-art techniques, finding that synthetic generated with our technique achieves good utility on average while also having good similarity scores, highlighting the potential of a mixed approach leveraging classical generation and model-driven discrimination for generating privacy-preserving, useful synthetic datasets.