Continuous Publication of Weighted Graphs with Local Differential Privacy
Summary: WGT-LDP: framework for continuous publication of weighted graph snapshots under w‑event edge-weight LDP, protecting edges and weights across any w consecutive steps. Combines population-division sampling, range estimation, aggregate collection and snapshot reconstruction with provable privacy/utility and strong empirical gains. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Wen Xu
- 2. Pengpeng Qiao
- 3. Shang Liu
- 4. Zhirun Zheng
- 5. Yang Cao
- 6. Zhetao Li
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 83 | Privacy Integrated Queries: An Extensible Platform for Privacy-Preserving Data Analysis | 2009 | SIGMOD | 0.00053933811 |
| 2,082 | Differentially Private Event Sequences over Infinite Streams | 2014 | VLDB | 9.5834599e-05 |
| 2,408 | Estimating Numerical Distributions under Local Differential Privacy | 2020 | SIGMOD | 8.8780076e-05 |
| 3,433 | LDP-IDS: Local Differential Privacy for Infinite Data Streams | 2022 | SIGMOD | 7.0998035e-05 |
| 3,469 | CGM: An Enhanced Mechanism for Streaming Data Collection with Local Differential Privacy | 2021 | VLDB | 7.0674518e-05 |
| 6,235 | Global and Local Differentially Private Release of Count-Weighted Graphs | 2023 | SIGMOD | 5.1451658e-05 |
| 6,410 | Publishing Attributed Social Graphs with Formal Privacy Guarantees | 2016 | SIGMOD | 5.0753667e-05 |
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