Alphabet growth improves canonical shuffled privacy precisely when the worst pairwise likelihood-ratio law collapses to a point mass, with sharp chi-squared bounds and optimal low-budget equivariant designs.
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Growing Alphabets in Canonical Shuffle Experiments: Likelihood-Ratio Laws, Estimation Bounds, and Low-Budget Equivariant Design
Alphabet growth improves canonical shuffled privacy precisely when the worst pairwise likelihood-ratio law collapses to a point mass, with sharp chi-squared bounds and optimal low-budget equivariant designs.