Architecting a Differentially Private SQL Engine
Summary: PRIVSQL: a system architecture for differentially private SQL query answering that decomposes the engine into independently optimizable components, bridging DP algorithm design and declarative DB systems. Prototype supports richer SQL and yields up to 7000× accuracy improvement, while identifying which components are mature versus underexplored for future DB research. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ios Kotsogiannis (Duke University)
- 2. Yuchao Tao (Duke University)
- 3. Ashwin Machanavajjhala (Duke University)
- 4. Gerome Miklau (University of Massachusetts Amherst)
- 5. Michael Hay (Colgate University)
BibTeX Citation
@inproceedings{kotsogiannis_cidr19,
address = {Amsterdam, Netherlands},
series = {{CIDR} '19},
title = {{Architecting a Differentially Private SQL Engine}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Kotsogiannis, Ios and Tao, Yuchao and Machanavajjhala, Ashwin and Miklau, Gerome and Hay, Michael},
year = {2019}
}Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,930 | IncShrink: Architecting Efficient Outsourced Databases using Incremental MPC and Differential Privacy | 2022 | SIGMOD | 6.4402718e-05 |
| 6,376 | DProvDB: Differentially Private Query Processing with Multi-Analyst Provenance | 2023 | SIGMOD | 5.893174e-05 |
| 8,719 | DPXPlain: Privately Explaining Aggregate Query Answers | 2023 | VLDB | 5.3774243e-05 |
| 9,248 | DPSAaS: Multi-Dimensional Data Sharing and Analytics as Services under Local Differential Privacy | 2019 | VLDB | 5.2987963e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 62 | Privacy Integrated Queries: An Extensible Platform for Privacy-Preserving Data Analysis | 2009 | SIGMOD | 0.00038970535 |
| 123 | Revealing Information while Preserving Privacy | 2003 | PODS | 0.00031082693 |
| 1,169 | PrivBayes: Private Data Release via Bayesian Networks | 2014 | SIGMOD | 0.00011838753 |
| 1,504 | A Data- and Workload-Aware Algorithm for Range Queries Under Differential Privacy | 2014 | VLDB | 0.00010548904 |
| 2,167 | Optimizing error of high-dimensional statistical queries under differential privacy | 2018 | VLDB | 9.047803e-05 |
| 2,300 | Principled Evaluation of Differentially Private Algorithms using DPBench | 2016 | SIGMOD | 8.7833594e-05 |
| 4,361 | ϵktelo: A Framework for Defining Differentially-Private Computations | 2018 | SIGMOD | 6.7443476e-05 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 6,534 | Better than Composition: How to Answer Multiple Relational Queries under Differential Privacy | 2023 | SIGMOD |
| 2 | 10,224 | DP-S4S: Accurate and Scalable Select-Join-Aggregate Query Processing with User-Level Differential Privacy | 2026 | SIGMOD |
| 3 | 10,335 | A General Framework for Per-record Differential Privacy | 2026 | SIGMOD |
| 4 | 6,441 | Differential Privacy in Data Publication and Analysis | 2012 | SIGMOD |
| 5 | 6,376 | DProvDB: Differentially Private Query Processing with Multi-Analyst Provenance | 2023 | SIGMOD |
| 6 | 6,666 | Residual Sensitivity for Differentially Private Multi-Way Joins | 2021 | SIGMOD |
| 7 | 62 | Privacy Integrated Queries: An Extensible Platform for Privacy-Preserving Data Analysis | 2009 | SIGMOD |
| 8 | 281 | Towards Practical Differential Privacy for SQL Queries | 2018 | VLDB |
| 9 | 11,318 | DOP-SQL: A General-purpose, High-utility, and Extensible Private SQL System | 2024 | VLDB |
| 10 | 1,144 | PrivateSQL: A Differentially Private SQL Query Engine | 2019 | VLDB |