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
- 2. Dhrubajyoti Ghosh
- 3. Xi He
- 4. Sharad Mehrotra
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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 |
|---|---|---|---|---|
| 65 | Privacy Integrated Queries: An Extensible Platform for Privacy-Preserving Data Analysis | 2009 | SIGMOD | 0.0003904561 |
| 133 | Revealing Information while Preserving Privacy | 2003 | PODS | 0.00030366593 |
| 298 | Towards Practical Differential Privacy for SQL Queries | 2018 | VLDB | 0.00022062013 |
| 1,319 | PrivTree: A Differentially Private Algorithm for Hierarchical Decompositions | 2016 | SIGMOD | 0.00011279242 |
| 1,581 | A Data- and Workload-Aware Algorithm for Range Queries Under Differential Privacy | 2014 | VLDB | 0.00010361616 |
| 1,959 | GUPT: Privacy Preserving Data Analysis Made Easy | 2012 | SIGMOD | 9.4917497e-05 |
| 5,596 | APEx: Accuracy-Aware Differentially Private Data Exploration | 2019 | SIGMOD | 6.2218599e-05 |
| 6,385 | IoT-Detective: Analyzing IoT Data Under Differential Privacy | 2018 | SIGMOD | 5.9581133e-05 |
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