HDPView: Differentially Private Materialized View for Exploring High Dimensional Relational Data

Summary: HDPView builds a DP materialized view for high-d data via recursive bisected partitioning of the count tensor, privately selecting cuts to minimize error. Offers workload independence, per-query error guarantees, compact storage, and noise resilience, with better performance than prior DP views. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 12866
Venue: VLDB
Year: 2022
Pagerank: 5.2333549e-05
Overall Rank: 9,717 | 33.34%
DOI: 10.14778/3538598.3538601

Incoming Non-self Citations Over Time

Authors

1. Fumiyuki Kato (Kyoto University)
2. Tsubasa Takahashi (LINE Corporation)
3. Shun Takagi (Kyoto University)
4. Yang Cao (Kyoto University)
5. Seng Pei Liew (LINE Corporation)
6. Masatoshi Yoshikawa (Kyoto University)

BibTeX Citation

@article{kato_vldb22,
        title = {{HDPView: Differentially Private Materialized View for Exploring High Dimensional Relational Data}},
        author = {Kato, Fumiyuki and Takahashi, Tsubasa and Takagi, Shun and Cao, Yang and Liew, Seng Pei and Yoshikawa, Masatoshi},
        journal = {PVLDB},
        series = {{VLDB} '22},
        volume = {15},
        number = {9},
        pages = {1766--1778},
        doi = {10.14778/3538598.3538601},
        url = {https://doi.org/10.14778/3538598.3538601},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
9,171	DP-starJ: A Differential Private Scheme towards Analytical Star-Join Queries	2023	SIGMOD	5.3093185e-05

Outgoing Citations (Sorted by Pagerank)

Showing 12 of 12 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
121	Boosting the Accuracy of Differentially Private Histograms Through Consistency	2010	VLDB	0.00031639377
281	Towards Practical Differential Privacy for SQL Queries	2018	VLDB	0.00022445849
1,144	PrivateSQL: A Differentially Private SQL Query Engine	2019	VLDB	0.00011999046
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
1,583	PriView: Practical Differentially Private Release of Marginal Contingency Tables	2014	SIGMOD	0.00010292522
2,167	Optimizing error of high-dimensional statistical queries under differential privacy	2018	VLDB	9.047803e-05
3,016	Plausible Deniability for Privacy-Preserving Data Synthesis	2017	VLDB	7.8484858e-05
3,412	Kamino: Constraint-Aware Differentially Private Data Synthesis	2021	VLDB	7.4344343e-05
4,261	Relational Data Synthesis using Generative Adversarial Networks: A Design Space Exploration	2020	VLDB	6.7982037e-05
5,055	APEx: Accuracy-Aware Differentially Private Data Exploration	2019	SIGMOD	6.3824509e-05

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