Infinite Stream Estimation under Personalized w-Event Privacy
Summary: Introduces personalized w-event privacy for infinite streams via a Personalized Window Size Mechanism (PWSM) to achieve (w,ε)-Personalized Differential Privacy, enabling per-user time-window privacy heterogeneity. Proposes two estimators—Personalized Budget Distribution (PBD) and Personalized Budget Absorption (PBA)—that allocate/absorb privacy budgets across slots, with provable guarantees and substantial empirical gains over uniform BD/BA (e.g., up to 68% and 24.9% error reductions). (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Leilei Du (Hunan University)
- 2. Peng Cheng (Tongji University)
- 3. Lei Chen (Hong Kong University of Science and Technology)
- 4. Heng Tao Shen (Tongji University; University of Electronic Science and Technology of China)
- 5. Xuemin Lin (Shanghai Jiao Tong University)
- 6. Wei Xi (Xi'an Jiaotong University)
BibTeX Citation
@article{du_vldb25,
title = {{Infinite Stream Estimation under Personalized w-Event Privacy}},
author = {Du, Leilei and Cheng, Peng and Chen, Lei and Shen, Heng Tao and Lin, Xuemin and Xi, Wei},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {6},
pages = {1905--1918},
doi = {10.14778/3725688.3725715},
url = {https://doi.org/10.14778/3725688.3725715},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,473 | MTSP-LDP: A Framework for Multi-Task Streaming Data Publication under Local Differential Privacy | 2026 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,074 | Differentially Private Event Sequences over Infinite Streams | 2014 | VLDB | 9.2192882e-05 |
| 3,113 | CGM: An Enhanced Mechanism for Streaming Data Collection with Local Differential Privacy | 2021 | VLDB | 7.7415187e-05 |
| 3,166 | LDP-IDS: Local Differential Privacy for Infinite Data Streams | 2022 | SIGMOD | 7.6752517e-05 |
| 4,446 | Projected Federated Averaging with Heterogeneous Differential Privacy | 2022 | VLDB | 6.6990707e-05 |
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