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arXiv · 2006.08522

Transparent Privacy is Principled Privacy

Ruobin Gong
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Abstract

In a technical treatment, this article establishes the necessity of transparent privacy for drawing unbiased statistical inference for a wide range of scientific questions. Transparency is a distinct feature enjoyed by differential privacy: the probabilistic mechanism with which the data are privatized can be made public without sabotaging the privacy guarantee. Uncertainty due to transparent privacy may be conceived as a dynamic and controllable component from the total survey error perspective. As the 2020 U.S. Decennial Census adopts differential privacy, constraints imposed on the privatized data products through optimization constitute a threat to transparency and result in limited statistical usability. Transparent privacy presents a viable path toward principled inference from privatized data releases, and shows great promise toward improved reproducibility, accountability, and public trust in modern data curation.

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Ruobin Gong. 2020-06-15. Transparent Privacy is Principled Privacy. https://doi.org/10.1162/99608f92.b5d3faaa

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