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, Karel De Schepper, Carlo Rossi Chauvenet
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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.
arXiv comments
20 pages, 4 tables, 8 figures; NeurIPS 2023 Workshop on Synthetic Data Generation with Generative AI
Journal reference
NeurIPS 2023 Workshop on Synthetic Data Generation with Generative AI