MIDE: Accuracy Aware Minimally Invasive Data Exploration For Decision Support
Summary: Introduces MIDE, an accuracy-aware, minimally-invasive data exploration framework for decision-support queries under privacy constraints. Adaptive privacy based on data distribution enforces bounded false negatives, improving naive privacy-accuracy tradeoffs; experiments show robustness across distributions. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Sameera Ghayyur (University of California Irvine)
- 2. Dhrubajyoti Ghosh (University of California Irvine)
- 3. Xi He (University of Waterloo)
- 4. Sharad Mehrotra (University of California Irvine)
BibTeX Citation
@article{ghayyur_vldb22,
title = {{MIDE: Accuracy Aware Minimally Invasive Data Exploration For Decision Support}},
author = {Ghayyur, Sameera and Ghosh, Dhrubajyoti and He, Xi and Mehrotra, Sharad},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {11},
pages = {2653--2665},
doi = {10.14778/3551793.3551821},
url = {https://doi.org/10.14778/3551793.3551821},
year = {2022}
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Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 64 | Privacy Integrated Queries: An Extensible Platform for Privacy-Preserving Data Analysis | 2009 | SIGMOD | 0.00038522486 |
| 122 | Revealing Information while Preserving Privacy | 2003 | PODS | 0.00030770793 |
| 276 | Towards Practical Differential Privacy for SQL Queries | 2018 | VLDB | 0.0002234348 |
| 1,324 | PrivTree: A Differentially Private Algorithm for Hierarchical Decompositions | 2016 | SIGMOD | 0.00011023355 |
| 1,528 | A Data- and Workload-Aware Algorithm for Range Queries Under Differential Privacy | 2014 | VLDB | 0.00010349919 |
| 1,955 | GUPT: Privacy Preserving Data Analysis Made Easy | 2012 | SIGMOD | 9.3176117e-05 |
| 5,184 | APEx: Accuracy-Aware Differentially Private Data Exploration | 2019 | SIGMOD | 6.2392459e-05 |
| 7,063 | IoT-Detective: Analyzing IoT Data Under Differential Privacy | 2018 | SIGMOD | 5.6100004e-05 |
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