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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)

Paper ID
2032
Venue
PODS
Year
2026
Pagerank
5.093636e-05
Overall Rank
10,157 | 30.32%
DOI
10.1145/3801902

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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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