PrivGene: Differentially Private Model Fitting Using Genetic Algorithms

Summary: PrivGene is a DP model-fitting framework using genetic algorithms to reduce perturbation and improve quality vs baselines. Novelty: enhanced exponential mechanism for model fitting; results on logistic regression, SVM, and k-means. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: h6e3fadd8fb2494ca
Venue: SIGMOD
Year: 2013
Pagerank: 5.6656994e-05
Overall Rank: 6,787 | 54.51%
DOI: 10.1145/2463676.2465330

Incoming Non-self Citations Over Time

Authors

1. Jun Zhang (Nanyang Technological University)
2. Xiaokui Xiao (Nanyang Technological University)
3. Yin Yang (Advanced Digital Sciences Center; University of Illinois Urbana-Champaign)
4. Zhenjie Zhang (Advanced Digital Sciences Center)
5. Marianne Winslett (Advanced Digital Sciences Center; University of Illinois Urbana-Champaign)

BibTeX Citation

@inproceedings{zhang_sigmod13,
        title = {{PrivGene: Differentially Private Model Fitting Using Genetic Algorithms}},
        author = {Zhang, Jun and Xiao, Xiaokui and Yang, Yin and Zhang, Zhenjie and Winslett, Marianne},
        series = {{SIGMOD} '13},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2463676.2465330},
        url = {https://dl.acm.org/doi/10.1145/2463676.2465330},
        year = {2013}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
1,160	PrivBayes: Private Data Release via Bayesian Networks	2014	SIGMOD	0.00011733273
1,334	PrivTree: A Differentially Private Algorithm for Hierarchical Decompositions	2016	SIGMOD	0.00010981739
3,721	Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics	2017	SIGMOD	7.0516515e-05

Outgoing Citations (Sorted by Pagerank)

Showing 9 of 9 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	0.00030972849
629	Optimizing Linear Counting Queries Under Differential Privacy	2010	PODS	0.00015421536
801	Differentially Private Data Cubes: Optimizing Noise Sources and Consistency	2011	SIGMOD	0.00013824207
1,256	No Free Lunch in Data Privacy	2011	SIGMOD	0.00011304781
1,458	PrivBasis: Frequent Itemset Mining with Differential Privacy	2012	VLDB	0.00010562758
1,966	GUPT: Privacy Preserving Data Analysis Made Easy	2012	SIGMOD	9.282558e-05
1,998	Low-Rank Mechanism: Optimizing Batch Queries under Differential Privacy	2012	VLDB	9.2114984e-05
5,171	A Rigorous and Customizable Framework for Privacy	2012	PODS	6.2321934e-05
5,307	Functional Mechanism: Regression Analysis under Differential Privacy	2012	VLDB	6.1741542e-05

Semantically Similar Papers

#	Overall Rank	Paper	Year	Venue
1	5,460	Optimal Differentially Private Algorithms for k-Means Clustering	2018	PODS
2	3,089	Plausible Deniability for Privacy-Preserving Data Synthesis	2017	VLDB
3	11,450	PrivEval: a tool for interactive evaluation of privacy metrics in synthetic data generation	2025	VLDB
4	2,462	Personalized Privacy Preservation	2006	SIGMOD
5	1,160	PrivBayes: Private Data Release via Bayesian Networks	2014	SIGMOD
6	69	Privacy-Preserving Data Mining	2000	SIGMOD
7	7,682	Private Incremental Regression	2017	PODS
8	256	On the Design and Quantification of Privacy Preserving Data Mining Algorithms	2001	PODS
9	8,022	Quantifying identifiability to choose and audit epsilon in differentially private deep learning	2021	VLDB
10	5,307	Functional Mechanism: Regression Analysis under Differential Privacy	2012	VLDB