DPXPlain: Privately Explaining Aggregate Query Answers
Summary: DPXPlain is the first DP framework for explaining group-by aggregate answers. It quantifies whether noisy comparisons are trustworthy, then privately returns approximately top-k explanation predicates with influence, rank, and confidence intervals. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yuchao Tao (Duke University)
- 2. Amir Gilad (Duke University)
- 3. Ashwin Machanavajjhala (Duke University)
- 4. Sudeepa Roy (Duke University)
BibTeX Citation
@article{tao_vldb23,
title = {{DPXPlain: Privately Explaining Aggregate Query Answers}},
author = {Tao, Yuchao and Gilad, Amir and Machanavajjhala, Ashwin and Roy, Sudeepa},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {1},
pages = {113--126},
doi = {10.14778/3561261.3561271},
url = {https://doi.org/10.14778/3561261.3561271},
year = {2023}
}Incoming Citations (Sorted by Pagerank)
Showing 8 of 8 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,576 | Toward Interpretable and Actionable Data Analysis with Explanations and Causality | 2022 | VLDB | 5.8364729e-05 |
| 6,932 | Summarized Causal Explanations For Aggregate Views | 2024 | SIGMOD | 5.7362362e-05 |
| 9,773 | Fair and Actionable Causal Prescription Ruleset | 2025 | SIGMOD | 5.2209769e-05 |
| 10,282 | Prism: Private Relational Data Synthesis with Language Models | 2026 | SIGMOD | 5.093636e-05 |
| 10,313 | Differentially Private Explanations for Clusters | 2026 | SIGMOD | 5.093636e-05 |
| 10,436 | Causal Explanations for Disparate Trends: Where and Why? | 2026 | SIGMOD | 5.093636e-05 |
| 10,979 | Finding Convincing Views to Endorse a Claim | 2025 | VLDB | 5.093636e-05 |
| 11,480 | Explaining Differentially Private Query Results With DPXPlain | 2023 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 32 of 32 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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