Continual Release of Differentially Private Synthetic Data from Longitudinal Data Collections
Summary: Proposes continual differential-privacy synthetic data for longitudinal studies, updating a consistent synthetic corpus as individuals add new data. Develops algorithms for fixed-window and cumulative queries, proves near-tight error bounds, and shows empirical performance on census-like data. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Mark Bun
- 2. Marco Gaboardi
- 3. Marcel Neunhoeffer
- 4. Wanrong Zhang
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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 |
|---|---|---|---|---|
| 111 | Privacy, Accuracy, and Consistency Too: A Holistic Solution to Contingency Table Release | 2007 | PODS | 0.00047073785 |
| 178 | Boosting the Accuracy of Differentially Private Histograms Through Consistency | 2010 | VLDB | 0.00037697111 |
| 2,082 | Differentially Private Event Sequences over Infinite Streams | 2014 | VLDB | 9.5834599e-05 |
| 2,894 | Pan-private Algorithms Via Statistics on Sketches | 2011 | PODS | 7.9474698e-05 |
| 3,329 | AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data | 2022 | VLDB | 7.2156424e-05 |
| 8,290 | Randomize the Future: Asymptotically Optimal Locally Private Frequency Estimation Protocol for Longitudinal Data | 2022 | PODS | 4.5435639e-05 |
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