Analysis of Shuffling Beyond Pure Local Differential Privacy
Summary: Introduces the shuffle index, a scalar χ capturing asymptotic privacy amplification beyond pure-LDP ε₀, and derives privacy bands with conditions for tightness. An FFT-based method computes blanket divergence at finite n with controlled error and near-linear time. (summarized by gpt-5.6-luna on Jul 26 2026)
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Authors
- 1. Shun Takagi (LY Corporation)
- 2. Seng Pei Liew (LY Corporation)
BibTeX Citation
@inproceedings{takagi_pods26,
address = {New York, NY, USA},
series = {{PODS} '26},
title = {{Analysis of Shuffling Beyond Pure Local Differential Privacy}},
url = {https://dl.acm.org/doi/10.1145/3801892},
doi = {10.1145/3801892},
booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
publisher = {Association for Computing Machinery},
author = {Takagi, Shun and Liew, Seng Pei},
year = {2026}
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Outgoing Citations (Sorted by Pagerank)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,595 | Privacy Amplification via Shuffling: Unified, Simplified, and Tightened | 2024 | VLDB | 6.1548101e-05 |
| 6,951 | RM2: Answer Counting Queries Efficiently under Shuffle Differential Privacy | 2025 | SIGMOD | 5.7303405e-05 |
| 8,489 | Network Shuffling: Privacy Amplification via Random Walks | 2022 | SIGMOD | 5.4152704e-05 |
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