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Efficient and Accurate Differentially Private Cardinality Continual Releases

Summary: Introduces FC, a differentially private continual-cardinality framework combining an efficient estimator with privacy mechanisms for real-time continual releases under low memory. Empirical results show up to 504× memory reduction with comparable accuracy, and order-of-magnitude accuracy gains under the same budget. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
7281
Venue
SIGMOD
Year
2025
Pagerank
5.093636e-05
Overall Rank
10,759 | 26.19%
DOI
10.1145/3725288

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Authors

BibTeX Citation

@inproceedings{xie_sigmod25,
        title = {{Efficient and Accurate Differentially Private Cardinality Continual Releases}},
        author = {Xie, Dongdong and Wang, Pinghui and Xu, Quanqing and Yang, Chuanhui and Li, Rundong},
        series = {{SIGMOD} '25},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3725288},
        url = {https://dl.acm.org/doi/10.1145/3725288},
        year = {2025}
}

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Rank Citing Paper Year Venue Pagerank
10,335 A General Framework for Per-record Differential Privacy 2026 SIGMOD 5.093636e-05
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