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Privacy Amplification via Shuffling: Unified, Simplified, and Tightened

Summary: Variation-ratio reduction: unified, tighter privacy-amplification framework for the shuffle model using total-variation and blanket probability-ratio parameters, covering both single- and multi-message protocols. Offers exact tightness for extremal randomizers, improved parallel-composition accounting, and an O(n) numeric amplifier with major privacy-budget savings. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13424
Venue
VLDB
Year
2024
Pagerank
4.7180617e-05
Overall Rank
7,484 | 47.94%
DOI
10.14778/3659437.3659444

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Rank Citing Paper Year Venue Pagerank
10,229 Doppio: Communication-Efficient and Secure Multi-Party Shuffle Differential Privacy 2026 VLDB 4.1945683e-05
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