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LDP-IDS: Local Differential Privacy for Infinite Data Streams

Summary: LDP-IDS: a w-event local differential privacy framework for infinite data streams. Online adaptive population division with recycling reduces noise and communication, enabling practical privacy across analytics with theoretical guarantees. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6558
Venue
SIGMOD
Year
2022
Pagerank
7.6752517e-05
Overall Rank
3,166 | 78.28%
DOI
10.1145/3514221.3526190

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{ren_sigmod22,
        title = {{LDP-IDS: Local Differential Privacy for Infinite Data Streams}},
        author = {Ren, Xuebin and Shi, Liang and Yu, Weiren and Yang, Shusen and Zhao, Cong and Xu, Zongben},
        series = {{SIGMOD} '22},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3514221.3526190},
        url = {https://dl.acm.org/doi/10.1145/3514221.3526190},
        year = {2022}
}

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