Privacy for Free: Leveraging Local Differential Privacy Perturbed Data from Multiple Services
Summary: Enables “free” privacy by aggregating each user’s heterogeneous LDP reports across services without extra privacy burden or shared mechanisms. UA/UWA optimize mean estimation, while ULE performs user-level likelihood estimation for distributions. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Rong Du (Hong Kong Polytechnic University)
- 2. Qingqing Ye (Hong Kong Polytechnic University)
- 3. Yue Fu (Hong Kong Polytechnic University)
- 4. Haibo Hu (Hong Kong Polytechnic University)
BibTeX Citation
@article{du_vldb25,
title = {{Privacy for Free: Leveraging Local Differential Privacy Perturbed Data from Multiple Services}},
author = {Du, Rong and Ye, Qingqing and Fu, Yue and Hu, Haibo},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {6},
pages = {1743--1755},
doi = {10.14778/3725688.3725703},
url = {https://doi.org/10.14778/3725688.3725703},
year = {2025}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,749 | Estimating Numerical Distributions under Local Differential Privacy | 2020 | SIGMOD | 8.1688076e-05 |
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