Optimizing Fitness-For-Use of Differentially Private Linear Queries

Summary: Treats DP for linear queries with per-query accuracy, noting matrix mechanisms optimize total error rather than per-query usefulness. Proposes Gaussian-noise strategy with optimized covariance to meet per-query accuracy while minimizing privacy cost. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 12546
Venue: VLDB
Year: 2021
Pagerank: 5.516666e-05
Overall Rank: 7,968 | 45.34%
DOI: 10.14778/3467861.3467864

Incoming Non-self Citations Over Time

Authors

1. Yingtai Xiao (Pennsylvania State University)
2. Zeyu Ding (Pennsylvania State University)
3. Yuxin Wang (Pennsylvania State University)
4. Danfeng Zhang (Pennsylvania State University)
5. Daniel Kifer (Pennsylvania State University)

BibTeX Citation

@article{xiao_vldb21,
        title = {{Optimizing Fitness-For-Use of Differentially Private Linear Queries}},
        author = {Xiao, Yingtai and Ding, Zeyu and Wang, Yuxin and Zhang, Danfeng and Kifer, Daniel},
        journal = {PVLDB},
        series = {{VLDB} '21},
        volume = {14},
        number = {10},
        pages = {1730--1742},
        doi = {10.14778/3467861.3467864},
        url = {https://doi.org/10.14778/3467861.3467864},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
6,376	DProvDB: Differentially Private Query Processing with Multi-Analyst Provenance	2023	SIGMOD	5.893174e-05
8,843	Cache Me If You Can: Accuracy-Aware Inference Engine for Differentially Private Data Exploration	2023	VLDB	5.3589295e-05
9,602	Answering Private Linear Queries Adaptively using the Common Mechanism	2023	VLDB	5.2487195e-05

Outgoing Citations (Sorted by Pagerank)

Showing 11 of 11 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	0.00031639377
123	Revealing Information while Preserving Privacy	2003	PODS	0.00031082693
567	Understanding Hierarchical Methods for Differentially Private Histograms	2013	VLDB	0.00016420715
775	Differentially Private Data Cubes: Optimizing Noise Sources and Consistency	2011	SIGMOD	0.00014110531
1,504	A Data- and Workload-Aware Algorithm for Range Queries Under Differential Privacy	2014	VLDB	0.00010548904
1,956	Low-Rank Mechanism: Optimizing Batch Queries under Differential Privacy	2012	VLDB	9.4157042e-05
2,108	An Adaptive Mechanism for Accurate Query Answering under Differential Privacy	2012	VLDB	9.1553397e-05
2,167	Optimizing error of high-dimensional statistical queries under differential privacy	2018	VLDB	9.047803e-05
5,008	Exploring Privacy-Accuracy Tradeoffs using DPComp	2016	SIGMOD	6.4027712e-05
5,555	Practical Differential Privacy via Grouping and Smoothing	2013	VLDB	6.1774651e-05
7,359	Pythia: Data Dependent Differentially Private Algorithm Selection	2017	SIGMOD	5.6341162e-05

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#	Overall Rank	Paper	Year	Venue
1	1,956	Low-Rank Mechanism: Optimizing Batch Queries under Differential Privacy	2012	VLDB
2	775	Differentially Private Data Cubes: Optimizing Noise Sources and Consistency	2011	SIGMOD
3	3,935	Output Perturbation with Query Relaxation	2008	VLDB
4	7,850	Differentially Private Data Release over Multiple Tables	2023	PODS
5	10,172	Weighted Fourier Factorizations: Optimal Gaussian Noise for Differentially Private Marginal and Product Queries	2026	PODS
6	7,898	A workload-adaptive mechanism for linear queries under local differential privacy	2020	VLDB
7	1,504	A Data- and Workload-Aware Algorithm for Range Queries Under Differential Privacy	2014	VLDB
8	2,108	An Adaptive Mechanism for Accurate Query Answering under Differential Privacy	2012	VLDB
9	9,602	Answering Private Linear Queries Adaptively using the Common Mechanism	2023	VLDB
10	611	Optimizing Linear Counting Queries Under Differential Privacy	2010	PODS