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DPXPlain: Privately Explaining Aggregate Query Answers

Summary: DPXPlain is the first DP framework for explaining group-by aggregate answers. It quantifies whether noisy comparisons are trustworthy, then privately returns approximately top-k explanation predicates with influence, rank, and confidence intervals. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
h9a5830160a798f21
Venue
VLDB
Year
2023
Pagerank
5.2567693e-05
Overall Rank
8,881 | 40.29%
DOI
10.14778/3561261.3561271

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{tao_vldb23,
        title = {{DPXPlain: Privately Explaining Aggregate Query Answers}},
        author = {Tao, Yuchao and Gilad, Amir and Machanavajjhala, Ashwin and Roy, Sudeepa},
        journal = {PVLDB},
        series = {{VLDB} '23},
        volume = {16},
        number = {1},
        pages = {113--126},
        doi = {10.14778/3561261.3561271},
        url = {https://doi.org/10.14778/3561261.3561271},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 9 of 9 citing papers.

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Outgoing Citations (Sorted by Pagerank)

Showing 32 of 32 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
17 Provenance Semirings 2007 PODS 0.00059752575
64 Privacy Integrated Queries: An Extensible Platform for Privacy-Preserving Data Analysis 2009 SIGMOD 0.00038522486
189 Scorpion: Explaining Away Outliers in Aggregate Queries 2013 VLDB 0.00025840026
276 Towards Practical Differential Privacy for SQL Queries 2018 VLDB 0.0002234348
391 Why Not? 2009 SIGMOD 0.00019238698
578 The Complexity of Causality and Responsibility for Query Answers and non-Answers 2011 VLDB 0.00016096504
622 On the Provenance of Non-Answers to Queries over Extracted Data 2008 VLDB 0.00015484312
670 A Formal Approach to Finding Explanations for Database Queries 2014 SIGMOD 0.00014954494
791 How to ConQueR Why-Not Questions 2010 SIGMOD 0.00013976192
819 Provenance for Aggregate Queries 2011 PODS 0.00013666629
878 Interpretable and Informative Explanations of Outcomes 2015 VLDB 0.00013302631
1,166 PrivateSQL: A Differentially Private SQL Query Engine 2019 VLDB 0.00011731286
1,453 PrivBasis: Frequent Itemset Mining with Differential Privacy 2012 VLDB 0.0001060277
2,067 Explaining Missing Answers to SPJUA Queries 2010 VLDB 9.0924963e-05
2,090 Optimizing error of high-dimensional statistical queries under differential privacy 2018 VLDB 9.0657103e-05
2,222 Explaining Query Answers with Explanation-Ready Databases 2016 VLDB 8.8109051e-05
2,458 Tracing Data Errors with View-Conditioned Causality 2011 SIGMOD 8.4371456e-05
2,488 Computing Local Sensitivities of Counting Queries with Joins 2020 SIGMOD 8.3973087e-05
2,722 The Impact of Negation on the Complexity of the Shapley Value in Conjunctive Queries 2020 PODS 8.0955983e-05
2,835 Fine-Grained, Secure and Efficient Data Provenance on Blockchain Systems 2019 VLDB 7.9553846e-05
3,175 Answering Range Queries Under Local Differential Privacy 2019 VLDB 7.5673688e-05
4,496 R2T: Instance-optimal Truncation for Differentially Private Query Evaluation with Foreign Keys 2022 SIGMOD 6.5764614e-05
4,739 Going Beyond Provenance: Explaining Query Answers with Pattern-based Counterbalances 2019 SIGMOD 6.4441962e-05
5,085 Differential Privacy and the US Census 2019 PODS 6.282326e-05
5,529 Putting Things into Context: Rich Explanations for Query Answers using Join Graphs 2021 SIGMOD 6.0935553e-05
5,673 Mining Frequent Patterns with Differential Privacy 2013 VLDB 6.0432078e-05
6,538 Provenance Views for Module Privacy 2011 PODS 5.7534198e-05
6,803 Residual Sensitivity for Differentially Private Multi-Way Joins 2021 SIGMOD 5.6783493e-05
7,295 Architecting a Differentially Private SQL Engine 2019 CIDR 5.5626111e-05
7,361 Enabling Privacy in Provenance-Aware Workflow Systems 2011 CIDR 5.5424891e-05
7,439 A Nearly Instance-optimal Differentially Private Mechanism for Conjunctive Queries 2022 PODS 5.5267455e-05
11,975 On Optimizing the Trade-off between Privacy and Utility in Data Provenance 2021 SIGMOD 4.9793485e-05
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