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DPSUR: Accelerating Differentially Private Stochastic Gradient Descent Using Selective Update and Release

Summary: DPSUR accelerates DP-SGD by validating noisy gradients and selectively applying only updates that improve convergence. Clipping-based randomization and thresholded selection address privacy, yielding faster convergence and higher utility across vision and text benchmarks. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
13556
Venue
VLDB
Year
2024
Pagerank
5.2151886e-05
Overall Rank
9,807 | 32.72%
DOI
10.14778/3648160.3648164

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{fu_vldb24,
        title = {{DPSUR: Accelerating Differentially Private Stochastic Gradient Descent Using Selective Update and Release}},
        author = {Fu, Jie and Ye, Qingqing and Hu, Haibo and Chen, Zhili and Wang, Lulu and Wang, Kuncan and Ran, Xun},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {6},
        pages = {1200--1213},
        doi = {10.14778/3648160.3648164},
        url = {https://doi.org/10.14778/3648160.3648164},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
5,746 Federated Heavy Hitter Analytics with Local Differential Privacy 2025 SIGMOD 6.1017514e-05
8,908 Privacy and Accuracy-Aware AI/ML Model Deduplication 2025 SIGMOD 5.3483178e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

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

Rank Cited Paper Year Venue Pagerank
11,444 Trajectory Data Collection with Local Differential Privacy 2023 VLDB 5.093636e-05
Previous Page 1 / 1 Next

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