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 |
|---|---|---|---|---|
| 4,414 | ϵktelo: A Framework for Defining Differentially-Private Computations | 2018 | SIGMOD | 6.6080858e-05 |
| 8,139 | Optimizing Fitness-For-Use of Differentially Private Linear Queries | 2021 | VLDB | 5.3905499e-05 |
| 8,599 | Differentially Private Hierarchical Count-of-Counts Histograms | 2018 | VLDB | 5.3045399e-05 |
| 9,750 | Free Gap Information from the Differentially Private Sparse Vector and Noisy Max Mechanisms | 2020 | VLDB | 5.1325223e-05 |
| 13,850 | DIAS: Differentially Private Interactive Algorithm Selection using Pythia | 2017 | SIGMOD | - |
Previous
Page 1 / 1
Next
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 | 0.0003107472 |
| 275 | Optimal Histograms with Quality Guarantees | 1998 | VLDB | 0.00022404363 |
| 558 | Understanding Hierarchical Methods for Differentially Private Histograms | 2013 | VLDB | 0.0001646292 |
| 1,530 | A Data- and Workload-Aware Algorithm for Range Queries Under Differential Privacy | 2014 | VLDB | 0.00010345019 |
| 2,301 | Principled Evaluation of Differentially Private Algorithms using DPBench | 2016 | SIGMOD | 8.6690677e-05 |
| 3,939 | DPT: Differentially Private Trajectory Synthesis Using Hierarchical Reference Systems | 2015 | VLDB | 6.9120876e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 1,530 | A Data- and Workload-Aware Algorithm for Range Queries Under Differential Privacy | 2014 | VLDB |
| 2 | 13,105 | Privacy in Data Systems | 2003 | PODS |
| 3 | 11,099 | Private Synthetic Data Generation in Bounded Memory | 2025 | PODS |
| 4 | 3,125 | Bayesian Differential Privacy on Correlated Data | 2015 | SIGMOD |
| 5 | 1,168 | PrivateSQL: A Differentially Private SQL Query Engine | 2019 | VLDB |
| 6 | 10,550 | A General Framework for Per-record Differential Privacy | 2026 | SIGMOD |
| 7 | 2,091 | Optimizing error of high-dimensional statistical queries under differential privacy | 2018 | VLDB |
| 8 | 1,156 | 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 | 13,850 | DIAS: Differentially Private Interactive Algorithm Selection using Pythia | 2017 | SIGMOD |