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Pufferfish Privacy Mechanisms for Correlated Data

Summary: Pufferfish privacy for correlated data (networks, time-series) is proposed. Wasserstein Mechanism enables general Pufferfish; a scalable practical variant suits time-series data, with experiments showing privacy-utility on synthetic and real domains. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5463
Venue
SIGMOD
Year
2017
Pagerank
6.7720437e-05
Overall Rank
4,302 | 70.49%
DOI
10.1145/3035918.3064025

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{song_sigmod17,
        title = {{Pufferfish Privacy Mechanisms for Correlated Data}},
        author = {Song, Shuang and Wang, Yizhen and Chaudhuri, Kamalika},
        series = {{SIGMOD} '17},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3035918.3064025},
        url = {https://dl.acm.org/doi/10.1145/3035918.3064025},
        year = {2017}
}

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