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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
13612
Venue
VLDB
Year
2024
Pagerank
6.1548101e-05
Overall Rank
5,595 | 61.62%
DOI
10.14778/3659437.3659444

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{wang_vldb24,
        title = {{Privacy Amplification via Shuffling: Unified, Simplified, and Tightened}},
        author = {Wang, Shaowei and Peng, Yun and Li, Jin and Wen, Zikai and Li, Zhipeng and Yu, Shiyu and Wang, Di and Yang, Wei},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {8},
        pages = {1870--1883},
        doi = {10.14778/3659437.3659444},
        url = {https://doi.org/10.14778/3659437.3659444},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
10,147 Analysis of Shuffling Beyond Pure Local Differential Privacy 2026 PODS 5.093636e-05
10,517 Doppio: Communication-Efficient and Secure Multi-Party Shuffle Differential Privacy 2026 VLDB 5.093636e-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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