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)
Incoming Non-self Citations Over Time
Authors
- 1. Shaowei Wang
- 2. Yun Peng
- 3. Jin Li
- 4. Zikai Wen
- 5. Zhipeng Li
- 6. Shiyu Yu
- 7. Di Wang
- 8. Wei Yang
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| 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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Outgoing Citations (Sorted by Pagerank)
Showing 14 of 14 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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Semantically Similar Papers
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| 177 | Limiting Privacy Breaches in Privacy Preserving Data Mining | 2003 | PODS | 0.0003788711 |
| 8,873 | Privacy Amplification by Sampling under User-level Differential Privacy | 2024 | SIGMOD | 4.4313867e-05 |
| 10,229 | Doppio: Communication-Efficient and Secure Multi-Party Shuffle Differential Privacy | 2026 | VLDB | 4.1945683e-05 |
| 4,794 | Optimal Random Perturbation at Multiple Privacy Levels | 2009 | VLDB | 5.9161511e-05 |
| 10,153 | Defense against Poisoning Attacks under Shuffle-DP | 2026 | SIGMOD | 4.1945683e-05 |
| 10,521 | RM2: Answer Counting Queries Efficiently under Shuffle Differential Privacy | 2025 | SIGMOD | 4.1945683e-05 |
| 5,229 | Improving Utility and Security of the Shuffler-based Differential Privacy | 2020 | VLDB | 5.6154535e-05 |
| 8,512 | Network Shuffling: Privacy Amplification via Random Walks | 2022 | SIGMOD | 4.4947966e-05 |