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)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
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)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,452 | DP-S4S: Accurate and Scalable Select-Join-Aggregate Query Processing with User-Level Differential Privacy | 2026 | SIGMOD | 4.9769913e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 17 of 17 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Previous
Page 1 / 1
Next