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Local Dampening: Differential Privacy for Non-numeric Queries via Local Sensitivity

Summary: Local Dampening: a generic differential-privacy mechanism for non-numeric queries via local sensitivity. Applied to Influential node analysis and private ID3, yields 3–4 orders of budget savings and up to 12% accuracy gain vs global DP baselines. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12760
Venue
VLDB
Year
2021
Pagerank
5.4688368e-05
Overall Rank
8,198 | 43.76%
DOI
10.14778/3436905.3436912

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{farias_vldb21,
        title = {{Local Dampening: Differential Privacy for Non-numeric Queries via Local Sensitivity}},
        author = {Farias, Victor A. E. and Brito, Felipe T. and Flynn, Cheryl and Machado, Javam C. and Majumdar, Subhabrata and Srivastava, Divesh},
        journal = {PVLDB},
        series = {{VLDB} '21},
        volume = {14},
        number = {4},
        pages = {521--533},
        doi = {10.14778/3436905.3436912},
        url = {https://doi.org/10.14778/3436905.3436912},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

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Rank Citing Paper Year Venue Pagerank
9,552 Practical and Accurate Local Edge Differentially Private Graph Algorithms 2025 VLDB 5.2528121e-05
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