DPXPlain: Privately Explaining Aggregate Query Answers
Summary: DPXPlain is a three-phase system for privately explaining group-by aggregate answers under differential privacy. It DP-answers queries, detects whether DP noise flips group comparisons, and outputs a top-k explanation table with predicate influences and confidence intervals, all DP-protected. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yuchao Tao
- 2. Amir Gilad
- 3. Ashwin Machanavajjhala
- 4. Sudeepa Roy
Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,565 | Toward Interpretable and Actionable Data Analysis with Explanations and Causality | 2022 | VLDB | 5.0081626e-05 |
| 7,172 | Summarized Causal Explanations For Aggregate Views | 2024 | SIGMOD | 4.8114797e-05 |
| 9,644 | Fair and Actionable Causal Prescription Ruleset | 2025 | SIGMOD | 4.3109001e-05 |
| 10,015 | Differentially Private Explanations for Clusters | 2026 | SIGMOD | 4.1945683e-05 |
| 10,147 | Causal Explanations for Disparate Trends: Where and Why? | 2026 | SIGMOD | 4.1945683e-05 |
| 10,740 | Finding Convincing Views to Endorse a Claim | 2025 | VLDB | 4.1945683e-05 |
| 11,281 | Explaining Differentially Private Query Results With DPXPlain | 2023 | VLDB | 4.1945683e-05 |
Previous
Page 1 / 1
Next
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.
Previous
Page 1 / 1
Next
Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,738 | PrivateSQL: A Differentially Private SQL Query Engine | 2019 | VLDB | 0.00010720057 |
| 11,112 | DOP-SQL: A General-purpose, High-utility, and Extensible Private SQL System | 2024 | VLDB | 4.1945683e-05 |
| 453 | Towards Practical Differential Privacy for SQL Queries | 2018 | VLDB | 0.00022741848 |
| 10,041 | A General Framework for Per-record Differential Privacy | 2026 | SIGMOD | 4.1945683e-05 |
| 2,465 | Principled Evaluation of Differentially Private Algorithms using DPBench | 2016 | SIGMOD | 8.7518123e-05 |
| 8,837 | Cache Me If You Can: Accuracy-Aware Inference Engine for Differentially Private Data Exploration | 2023 | VLDB | 4.4393184e-05 |
| 6,065 | APEx: Accuracy-Aware Differentially Private Data Exploration | 2019 | SIGMOD | 5.2291685e-05 |
| 13,099 | Demonstration of DPClustX: Differentially Private Explanations for Clusters | 2025 | SIGMOD | - |
| 10,015 | Differentially Private Explanations for Clusters | 2026 | SIGMOD | 4.1945683e-05 |
| 11,281 | Explaining Differentially Private Query Results With DPXPlain | 2023 | VLDB | 4.1945683e-05 |