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Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics

Summary: Bolt-on differential privacy for scalable SGD via output perturbation; seamless integration into Bismarck on a relational DBMS. Novel L2-sensitivity analysis of SGD under limited passes; improved convergence, negligible overhead, and up to 4x test accuracy over prior DP-SGD. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5485
Venue
SIGMOD
Year
2017
Pagerank
6.8462927e-05
Overall Rank
4,185 | 71.29%
DOI
10.1145/3035918.3064047

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{wu_sigmod17,
        title = {{Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics}},
        author = {Wu, Xi and Li, Fengan and Kumar, Arun and Chaudhuri, Kamalika and Jha, Somesh and Naughton, Jeffrey},
        series = {{SIGMOD} '17},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3035918.3064047},
        url = {https://dl.acm.org/doi/10.1145/3035918.3064047},
        year = {2017}
}

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