Privately Answering Queries on Skewed Data via Per-Record Differential Privacy
Summary: Propose per-record zero-concentrated differential privacy (PzCDP), a formalism that makes each record's privacy loss a public function of its confidential value (data-dependent budget). Develop properties and mechanisms for skewed/heavy-tailed statistics to accurately release outlier-driven aggregates while relaxing standard DP noise constraints. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jeremy Seeman (Tumult Labs)
- 2. William Sexton (Tumult Labs)
- 3. David Pujol (Tumult Labs)
- 4. Ashwin Machanavajjhala (Tumult Labs)
BibTeX Citation
@article{seeman_vldb24,
title = {{Privately Answering Queries on Skewed Data via Per-Record Differential Privacy}},
author = {Seeman, Jeremy and Sexton, William and Pujol, David and Machanavajjhala, Ashwin},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {11},
pages = {3138--3150},
doi = {10.14778/3681954.3681989},
url = {https://doi.org/10.14778/3681954.3681989},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| 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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Outgoing Citations (Sorted by Pagerank)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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