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Personalized Truncation for Personalized Privacy

Summary: Proposes a PDP personalized truncation mechanism for counting and sum estimation. Theoretically, it matches or beats prior PDP methods up to polylog factors and gains in favorable cases; experiments show empirical advantages and applicability to user-level DP for SJA queries under foreign-key constraints. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
7057
Venue
SIGMOD
Year
2024
Pagerank
5.093636e-05
Overall Rank
11,203 | 23.14%
DOI
10.1145/3698825

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Authors

BibTeX Citation

@inproceedings{sun_sigmod24,
        title = {{Personalized Truncation for Personalized Privacy}},
        author = {Sun, Dajun and Dong, Wei and Qiu, Yuan and Yi, Ke},
        series = {{SIGMOD} '24},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3698825},
        url = {https://dl.acm.org/doi/10.1145/3698825},
        year = {2024}
}

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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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