Improved Accuracy for Private Continual Cardinality Estimation in Fully Dynamic Streams via Matrix Factorization
Summary: Improves private continual cardinality estimation for fully dynamic streams by analyzing sensitivity vectors of difference-stream reductions. Tight matrix-factorization analyses—especially square-root factorizations—yield better DP accuracy for distinct counts, degree histograms, and triangle counts. (summarized by gpt-5.6-luna on Jul 26 2026)
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Authors
- 1. Joel Daniel Andersson (Institute of Science and Technology Austria)
- 2. Palak Jain (Boston University)
- 3. Satchit Sivakumar (Boston University)
BibTeX Citation
@inproceedings{andersson_pods26,
address = {New York, NY, USA},
series = {{PODS} '26},
title = {{Improved Accuracy for Private Continual Cardinality Estimation in Fully Dynamic Streams via Matrix Factorization}},
url = {https://dl.acm.org/doi/10.1145/3801902},
doi = {10.1145/3801902},
booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
publisher = {Association for Computing Machinery},
author = {Andersson, Joel Daniel and Jain, Palak and Sivakumar, Satchit},
year = {2026}
}
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 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 | 0.00031639377 |
| 567 | Understanding Hierarchical Methods for Differentially Private Histograms | 2013 | VLDB | 0.00016420715 |
| 6,826 | Fully Dynamic Algorithms for Graph Databases with Edge Differential Privacy | 2025 | PODS | 5.7621757e-05 |
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