DP-S4S: Accurate and Scalable Select-Join-Aggregate Query Processing with User-Level Differential Privacy
Summary: DP-S4S enables scalable user-level DP for select-join-aggregate queries by sampling aggregation units rather than users and developing an RDP-friendly foundation. It supports scalar and GROUP BY queries with substantially better accuracy than prior sampling-based S&E. (summarized by gpt-5.6-luna on Jul 26 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Yuan Qiu (Southeast University)
- 2. Xiaokui Xiao (National University of Singapore)
- 3. Yin Yang (Hamad Bin Khalifa University)
BibTeX Citation
@inproceedings{qiu_sigmod26,
title = {{DP-S4S: Accurate and Scalable Select-Join-Aggregate Query Processing with User-Level Differential Privacy}},
author = {Qiu, Yuan and Xiao, Xiaokui and Yang, Yin},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3802042},
url = {https://dl.acm.org/doi/10.1145/3802042},
year = {2026}
}Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 9 of 9 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,144 | PrivateSQL: A Differentially Private SQL Query Engine | 2019 | VLDB | 0.00011999046 |
| 4,394 | R2T: Instance-optimal Truncation for Differentially Private Query Evaluation with Foreign Keys | 2022 | SIGMOD | 6.7274063e-05 |
| 5,055 | APEx: Accuracy-Aware Differentially Private Data Exploration | 2019 | SIGMOD | 6.3824509e-05 |
| 6,376 | DProvDB: Differentially Private Query Processing with Multi-Analyst Provenance | 2023 | SIGMOD | 5.893174e-05 |
| 6,534 | Better than Composition: How to Answer Multiple Relational Queries under Differential Privacy | 2023 | SIGMOD | 5.8486828e-05 |
| 7,456 | Privacy Amplification by Sampling under User-level Differential Privacy | 2024 | SIGMOD | 5.6124452e-05 |
| 8,158 | Continual Observation of Joins under Differential Privacy | 2024 | SIGMOD | 5.4756587e-05 |
| 8,908 | Privacy and Accuracy-Aware AI/ML Model Deduplication | 2025 | SIGMOD | 5.3483178e-05 |
| 10,914 | Calibrating Noise for Group Privacy in Subsampled Mechanisms | 2025 | VLDB | 5.093636e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 11,203 | Personalized Truncation for Personalized Privacy | 2024 | SIGMOD |
| 2 | 1,144 | PrivateSQL: A Differentially Private SQL Query Engine | 2019 | VLDB |
| 3 | 9,330 | Differentially Private Stream Processing at Scale | 2024 | VLDB |
| 4 | 11,318 | DOP-SQL: A General-purpose, High-utility, and Extensible Private SQL System | 2024 | VLDB |
| 5 | 6,666 | Residual Sensitivity for Differentially Private Multi-Way Joins | 2021 | SIGMOD |
| 6 | 10,542 | Secure Multi-Party Sampling over Joins | 2026 | VLDB |
| 7 | 7,292 | A Nearly Instance-optimal Differentially Private Mechanism for Conjunctive Queries | 2022 | PODS |
| 8 | 836 | Recursive Mechanism: Towards Node Differential Privacy and Unrestricted Joins | 2013 | SIGMOD |
| 9 | 7,147 | Architecting a Differentially Private SQL Engine | 2019 | CIDR |
| 10 | 7,456 | Privacy Amplification by Sampling under User-level Differential Privacy | 2024 | SIGMOD |