What question did this study set out to answer?

This research aims to address the analyst-side identifiability problem in differential privacy mechanisms and its implications for statistical inference.

June 12, 2026Open Access

Coarsening at random for differential privacy: analyst-side identifiability under privacy mechanisms

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This research aims to address the analyst-side identifiability problem in differential privacy mechanisms and its implications for statistical inference.
Analyzed coarsening-at-random conditions partitioning differential privacy mechanisms.
Established several theorems regarding identifiability, including a glass-ceiling theorem and a release-consistency theorem.
Conducted a base-R simulation to confirm theoretical results and explore kernel-dependence.
Identifiability is compromised under data-dependent mechanisms, violating symmetry conditions.
Discoveries highlight a glass-ceiling effect on non-identifiability without mechanism knowledge.
Simulation results verify the expected release consistency despite biased estimates.

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Implication

Randomized trial explores analyst-side identifiability in differential privacy, indicating implications for statistical inference.

Cite This Study

Alexander Towell (2026) studied this question.

synapsesocial.com/papers/6a2ba4a18101cf8926f03043 https://doi.org/10.5281/zenodo.20422886

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Coarsening at random for differential privacy: analyst-side identifiability under privacy mechanisms

Key Points

Discussion

Implication

Cite This Study

Coarsening at random for differential privacy: analyst-side identifiability under privacy mechanisms

Key Points

Discussion

Implication

Cite This Study

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