Privacy Measurement in Tabular Synthetic Data: State of the Art and Future Research Directions

Alexander Boudewijn, Andrea Filippo Ferraris, Daniele Panfilo, Vanessa Cocca, Sabrina Zinutti +2 more
11/29/2023
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Abstract

Synthetic data (SD) have garnered attention as a privacy enhancing technology. Unfortunately, there is no standard for quantifying their degree of privacy protection. In this paper, we discuss proposed quantification approaches. This contributes to the development of SD privacy standards; stimulates multi-disciplinary discussion; and helps SD researchers make informed modeling and evaluation decisions.

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Cite this paper

@article{boudewijn2023privacy,
  title  = {Privacy Measurement in Tabular Synthetic Data: State of the Art and Future Research Directions},
  author = {Alexander Boudewijn and Andrea Filippo Ferraris and Daniele Panfilo and Vanessa Cocca and Sabrina Zinutti and Karel De Schepper and Carlo Rossi Chauvenet},
  year   = {2023},
  eprint = {2311.17453},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2311.17453v1}
}

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