Privacy Measurement in Tabular Synthetic Data: State of the Art and Future Research Directions
This work tackles the problem of establishing privacy measurement standards for synthetic data, which is crucial for researchers and practitioners in data privacy, but it is incremental as it reviews and discusses existing approaches rather than introducing new methods.
The paper addresses the lack of a standard for measuring privacy in tabular synthetic data by reviewing existing quantification approaches, aiming to contribute to privacy standards and guide researchers in modeling and evaluation.
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.