Pythia: Data Dependent Differentially Private Algorithm Selection
Summary: Pythia, a data-dependent meta-algorithm, learns data properties to pick the best DP algorithm. End-to-end DP system that tests low-sensitivity properties and applies the chosen algorithm, improving histograms, 1- and 2-D range queries, and Naive Bayes. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ios Kotsogiannis (Duke University)
- 2. Ashwin Machanavajjhala (Duke University)
- 3. Michael Hay (Colgate University)
- 4. Gerome Miklau (University of Massachusetts Amherst)
BibTeX Citation
@inproceedings{kotsogiannis_sigmod17,
title = {{Pythia: Data Dependent Differentially Private Algorithm Selection}},
author = {Kotsogiannis, Ios and Machanavajjhala, Ashwin and Hay, Michael and Miklau, Gerome},
series = {{SIGMOD} '17},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3035918.3035945},
url = {https://dl.acm.org/doi/10.1145/3035918.3035945},
year = {2017}
}
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,412 | ϵktelo: A Framework for Defining Differentially-Private Computations | 2018 | SIGMOD | 6.6112155e-05 |
| 8,133 | Optimizing Fitness-For-Use of Differentially Private Linear Queries | 2021 | VLDB | 5.3931029e-05 |
| 8,592 | Differentially Private Hierarchical Count-of-Counts Histograms | 2018 | VLDB | 5.3069959e-05 |
| 9,745 | Free Gap Information from the Differentially Private Sparse Vector and Noisy Max Mechanisms | 2020 | VLDB | 5.1349531e-05 |
| 13,845 | DIAS: Differentially Private Interactive Algorithm Selection using Pythia | 2017 | SIGMOD | - |
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 121 | Boosting the Accuracy of Differentially Private Histograms Through Consistency | 2010 | VLDB | 0.00031089378 |
| 275 | Optimal Histograms with Quality Guarantees | 1998 | VLDB | 0.00022413521 |
| 558 | Understanding Hierarchical Methods for Differentially Private Histograms | 2013 | VLDB | 0.00016470707 |
| 1,528 | A Data- and Workload-Aware Algorithm for Range Queries Under Differential Privacy | 2014 | VLDB | 0.00010349919 |
| 2,298 | Principled Evaluation of Differentially Private Algorithms using DPBench | 2016 | SIGMOD | 8.6731734e-05 |
| 3,938 | DPT: Differentially Private Trajectory Synthesis Using Hierarchical Reference Systems | 2015 | VLDB | 6.9153613e-05 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 1,528 | A Data- and Workload-Aware Algorithm for Range Queries Under Differential Privacy | 2014 | VLDB |
| 2 | 13,099 | Privacy in Data Systems | 2003 | PODS |
| 3 | 11,090 | Private Synthetic Data Generation in Bounded Memory | 2025 | PODS |
| 4 | 3,123 | Bayesian Differential Privacy on Correlated Data | 2015 | SIGMOD |
| 5 | 1,166 | PrivateSQL: A Differentially Private SQL Query Engine | 2019 | VLDB |
| 6 | 10,539 | A General Framework for Per-record Differential Privacy | 2026 | SIGMOD |
| 7 | 2,090 | Optimizing error of high-dimensional statistical queries under differential privacy | 2018 | VLDB |
| 8 | 1,155 | PrivBayes: Private Data Release via Bayesian Networks | 2014 | SIGMOD |
| 9 | 64 | Privacy Integrated Queries: An Extensible Platform for Privacy-Preserving Data Analysis | 2009 | SIGMOD |
| 10 | 13,845 | DIAS: Differentially Private Interactive Algorithm Selection using Pythia | 2017 | SIGMOD |