Full Citation

Title: Transparent Privacy is Principled Privacy

Citation Type: Working Paper

Publication Year: 2022

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Abstract: Differential privacy revolutionizes the way we think about statistical disclosure limitation. A distinct feature of differential privacy is that the probabilistic mechanism with which the data are privatized can be made public without sabotaging the privacy guarantee. In a technical treatment, this paper establishes the necessity of transparent privacy for drawing unbiased statistical inference for a wide range of scientific questions. Uncertainty due to privacy may be conceived as a dynamic and controllable component from the total survey error perspective. Mandated invariants constitute a threat to transparency when imposed on the privatized data product through "post-processing", resulting in limited statistical usability. Transparent privacy presents a viable path towards principled inference from privatized data releases, and shows great promise towards improved reproducibility, accountability and public trust in modern data curation.

Url: https://arxiv.org/pdf/2006.08522.pdf

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Authors: Gong, Ruobin

Series Title: Harvard Data Science Review

Publication Number: Special Issue 2

Institution: Department of Statistics, Rutgers University

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Data Collections: IPUMS NHGIS

Topics: Other, Population Data Science

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