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iReduct: Differential Privacy with Reduced Relative Errors

Summary: iReduct provides differential privacy with reduced relative errors by allocating noise adaptively across query results. A novel resampling-based correlated-noise technique improves utility for small vs large answers, demonstrated on marginals of multi-dimensional histograms with real data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4447
Venue
SIGMOD
Year
2011
Pagerank
8.1125988e-05
Overall Rank
2,799 | 80.80%
DOI
10.1145/1989323.1989348

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{xiao_sigmod11,
        title = {{iReduct: Differential Privacy with Reduced Relative Errors}},
        author = {Xiao, Xiaokui and Bender, Gabriel and Hay, Michael and Gehrke, Johannes},
        series = {{SIGMOD} '11},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1989323.1989348},
        url = {https://dl.acm.org/doi/10.1145/1989323.1989348},
        year = {2011}
}

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