Privacy for Free: Leveraging Local Differential Privacy Perturbed Data from Multiple Services
Summary: Aggregates heterogeneous LDP-perturbed reports from multiple services in a mechanism- and statistic-agnostic way, avoiding extra per-service privacy cost. Introduces Unbiased Averaging, variance-optimal User-level Weighted Averaging for means, and User-level Likelihood Estimation for distributions, improving estimation accuracy. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Rong Du
- 2. Qingqing Ye
- 3. Yue Fu
- 4. Haibo Hu
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,408 | Estimating Numerical Distributions under Local Differential Privacy | 2020 | SIGMOD | 8.8780076e-05 |
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