DP-starJ: A Differential Private Scheme towards Analytical Star-Join Queries

Summary: DP-starJ provides a DP framework for star-join queries, addressing asymmetries between fact and dimension tables. Predicate Mechanism perturbs join predicates, not results, with a DP-compliant star-join algorithm; results show improved accuracy. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 6802
Venue: SIGMOD
Year: 2023
Pagerank: 5.3093185e-05
Overall Rank: 9,171 | 37.08%
DOI: 10.1145/3626725

Incoming Non-self Citations Over Time

Authors

1. Congcong Fu (Xidian University)
2. Hui Li (Xidian University; Yunxi Technology Co., Ltd)
3. Jian Lou (Zhejiang University)
4. Huizhen Li (Xidian University)
5. Jiangtao Cui (Xidian University)

BibTeX Citation

@inproceedings{fu_sigmod23,
        title = {{DP-starJ: A Differential Private Scheme towards Analytical Star-Join Queries}},
        author = {Fu, Congcong and Li, Hui and Lou, Jian and Li, Huizhen and Cui, Jiangtao},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3626725},
        url = {https://dl.acm.org/doi/10.1145/3626725},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
8,249	Femur: A Flexible Framework for Fast and Secure Querying from Public Key-Value Store	2025	SIGMOD	5.4574671e-05
10,221	Differentially Oblivious Multi-way Join	2026	SIGMOD	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 17 of 17 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
130	Privacy, Accuracy, and Consistency Too: A Holistic Solution to Contingency Table Release	2007	PODS	0.00030604781
281	Towards Practical Differential Privacy for SQL Queries	2018	VLDB	0.00022445849
567	Understanding Hierarchical Methods for Differentially Private Histograms	2013	VLDB	0.00016420715
1,144	PrivateSQL: A Differentially Private SQL Query Engine	2019	VLDB	0.00011999046
1,220	Calibrating Data to Sensitivity in Private Data Analysis: A Platform for Differentially-Private Analysis of Weighted Datasets	2014	VLDB	0.00011619529
1,583	PriView: Practical Differentially Private Release of Marginal Contingency Tables	2014	SIGMOD	0.00010292522
1,790	Publishing Graph Degree Distribution with Node Differential Privacy	2016	SIGMOD	9.7502278e-05
2,436	Computing Local Sensitivities of Counting Queries with Joins	2020	SIGMOD	8.5806427e-05
4,106	Secure Shapley Value for Cross-Silo Federated Learning	2023	VLDB	6.8980966e-05
4,394	R2T: Instance-optimal Truncation for Differentially Private Query Evaluation with Foreign Keys	2022	SIGMOD	6.7274063e-05
4,827	PrivLava: Synthesizing Relational Data with Foreign Keys under Differential Privacy	2023	SIGMOD	6.4895329e-05
6,666	Residual Sensitivity for Differentially Private Multi-Way Joins	2021	SIGMOD	5.8086805e-05
6,804	A Neural Database for Differentially Private Spatial Range Queries	2022	VLDB	5.768025e-05
7,292	A Nearly Instance-optimal Differentially Private Mechanism for Conjunctive Queries	2022	PODS	5.653597e-05
8,489	Network Shuffling: Privacy Amplification via Random Walks	2022	SIGMOD	5.4152704e-05
9,717	HDPView: Differentially Private Materialized View for Exploring High Dimensional Relational Data	2022	VLDB	5.2333549e-05

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