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 (Boston University)
- 2. Marco Gaboardi (Boston University)
- 3. Marcel Neunhoeffer (Institute for Employment Research; Ludwig Maximilian University of Munich)
- 4. Wanrong Zhang (Harvard University)
BibTeX Citation
@inproceedings{bun_pods24,
address = {New York, NY, USA},
series = {{PODS} '24},
title = {{Continual Release of Differentially Private Synthetic Data from Longitudinal Data Collections}},
url = {https://dl.acm.org/doi/10.1145/3651595},
doi = {10.1145/3651595},
booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
publisher = {Association for Computing Machinery},
author = {Bun, Mark and Gaboardi, Marco and Neunhoeffer, Marcel and Zhang, Wanrong},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
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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 |
|---|---|---|---|---|
| 121 | Boosting the Accuracy of Differentially Private Histograms Through Consistency | 2010 | VLDB | 0.00030972849 |
| 134 | Privacy, Accuracy, and Consistency Too: A Holistic Solution to Contingency Table Release | 2007 | PODS | 0.00029947443 |
| 2,137 | Differentially Private Event Sequences over Infinite Streams | 2014 | VLDB | 8.9783832e-05 |
| 2,917 | AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data | 2022 | VLDB | 7.8530527e-05 |
| 3,064 | Pan-private Algorithms Via Statistics on Sketches | 2011 | PODS | 7.6775964e-05 |
| 8,716 | Randomize the Future: Asymptotically Optimal Locally Private Frequency Estimation Protocol for Longitudinal Data | 2022 | PODS | 5.2705676e-05 |
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