Calibrating Noise for Group Privacy in Subsampled Mechanisms
Summary: Introduces tight privacy accounting for group privacy in subsampled mechanisms, avoiding pessimistic black-box DP-to-GP conversion. Exploiting subsampling randomness yields over an order-of-magnitude noise reductions, including for GP-SGD deep-learning training. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Yangfan Jiang (National University of Singapore)
- 2. Xinjian Luo (National University of Singapore)
- 3. Yin Yang (Hamad Bin Khalifa University)
- 4. Xiaokui Xiao (National University of Singapore)
BibTeX Citation
@article{jiang_vldb25,
title = {{Calibrating Noise for Group Privacy in Subsampled Mechanisms}},
author = {Jiang, Yangfan and Luo, Xinjian and Yang, Yin and Xiao, Xiaokui},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {2},
pages = {322--334},
doi = {10.14778/3705829.3705848},
url = {https://doi.org/10.14778/3705829.3705848},
year = {2025}
}
Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,224 | DP-S4S: Accurate and Scalable Select-Join-Aggregate Query Processing with User-Level Differential Privacy | 2026 | SIGMOD | 5.093636e-05 |
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