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March 4, 20260 citationsOpen Access

Universal Shuffle Asymptotics, Part II: Non-Gaussian Limits for Shuffle Privacy — Poisson, Skellam, and Point Process Regimes

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ASAlex Shvets

Key Points

  • This research aims to characterize non-Gaussian limits for shuffle differential privacy and identify universality-breaking fronts in concentration of local randomizers.
  • Performed Poisson-shift limit experiments for neighboring pairs under a set convergence rate.
  • Analyzed Skellam-shift limits for proportional compositions.
  • Investigated multivariate Poisson point processes in a sparse-error critical regime.
  • Developed a three-regime phase diagram encompassing different privacy scenarios.
  • Identified critical sequences where classical conditions fail and jump phenomena occur.
  • Established explicit convergence rates in Le Cam distance for critical sequences.
  • Demonstrated the disappearance of certain error floors in specific boundary compositions.
  • Unified findings from Parts I and II in a comprehensive phase diagram.

Abstract

Part II of the Universal Shuffle Asymptotics series. Characterizes the first universality-breaking frontier for shuffle differential privacy: critical sequences of increasingly concentrated local randomizers where classical Lindeberg conditions fail and the shuffle score exhibits rare macroscopic jumps. Main results: (1) Poisson-shift limit experiment for the canonical neighboring pair when exp(ε₀(n))/n → c², with explicit O(1/n) Le Cam convergence rate and a support-mismatch δ-floor; (2) Skellam-shift limit for proportional compositions k/n → π ∈ (0,1), with disappearance of the δ-floor away from boundary compositions; (3) multivariate Poisson point process / compound-Poisson limit for general finite alphabets under a sparse-error critical regime, yielding explicit limiting (ε,δ) curves as Poisson series; (4) a three-regime phase diagram (sub-critical Gaussian, critical Poisson/Skellam/PPP, super-critical no privacy) unifying Parts I and II. All convergence results are at the level of binary experiments and Le Cam distance with explicit total-variation bounds. Includes reproducible Python code for numerical illustrations. Companion to Part I (Sharp Privacy Analysis in the Gaussian Regime).

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Cite This Study

Alex Shvets (2026) studied this question.

synapsesocial.com/papers/69a7cd5ed48f933b5eed99c7https://doi.org/10.5281/zenodo.18841286
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