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)

Paper ID: 12941
Venue: VLDB
Year: 2022
Pagerank: 5.093636e-05
Overall Rank: 11,580 | 20.56%
DOI: 10.14778/3551793.3551821

Incoming Non-self Citations Over Time

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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}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

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
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
281	Towards Practical Differential Privacy for SQL Queries	2018	VLDB	0.00022445849
1,308	PrivTree: A Differentially Private Algorithm for Hierarchical Decompositions	2016	SIGMOD	0.00011216361
1,504	A Data- and Workload-Aware Algorithm for Range Queries Under Differential Privacy	2014	VLDB	0.00010548904
1,906	GUPT: Privacy Preserving Data Analysis Made Easy	2012	SIGMOD	9.5020196e-05
5,055	APEx: Accuracy-Aware Differentially Private Data Exploration	2019	SIGMOD	6.3824509e-05
6,922	IoT-Detective: Analyzing IoT Data Under Differential Privacy	2018	SIGMOD	5.7387625e-05

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