Privacy Measurement in Tabular Synthetic Data: State of the Art and Future Research Directions
CoRR(2023)
摘要
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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