CGM: An Enhanced Mechanism for Streaming Data Collection with Local Differential Privacy
Summary: CGM adapts Gaussian LDP to streaming data by injecting temporally correlated noise optimized for public autocorrelation, value ranges, and utility. It preserves (ε,δ)-LDP while substantially improving trend fidelity over repeated one-shot perturbation. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ergute Bao (National University of Singapore)
- 2. Yin Yang (Hamad Bin Khalifa University)
- 3. Xiaokui Xiao (National University of Singapore)
- 4. Bolin Ding (Alibaba)
BibTeX Citation
@article{bao_vldb21,
title = {{CGM: An Enhanced Mechanism for Streaming Data Collection with Local Differential Privacy}},
author = {Bao, Ergute and Yang, Yin and Xiao, Xiaokui and Ding, Bolin},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {11},
pages = {2258--2270},
doi = {10.14778/3476249.3476277},
url = {https://doi.org/10.14778/3476249.3476277},
year = {2021}
}
Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,678 | Heavy Hitters and the Structure of Local Privacy | 2018 | PODS | 0.00010032135 |
| 1,837 | Marginal Release Under Local Differential Privacy | 2018 | SIGMOD | 9.6443703e-05 |
| 2,279 | Answering Multi-Dimensional Analytical Queries under Local Differential Privacy | 2019 | SIGMOD | 8.8155688e-05 |
| 4,185 | Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics | 2017 | SIGMOD | 6.8462927e-05 |
| 6,922 | IoT-Detective: Analyzing IoT Data Under Differential Privacy | 2018 | SIGMOD | 5.7387625e-05 |
| 9,248 | DPSAaS: Multi-Dimensional Data Sharing and Analytics as Services under Local Differential Privacy | 2019 | VLDB | 5.2987963e-05 |
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