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Privacy at Scale: Local Differential Privacy in Practice

Summary: Local differential privacy at Internet scale: deployments by Google, Apple, Microsoft for telemetry. Survey theory and algorithms; discuss data-management implications and open directions for scalable privacy analytics without a trusted server. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5651
Venue
SIGMOD
Year
2018
Pagerank
8.4460386e-05
Overall Rank
2,542 | 82.57%
DOI
10.1145/3183713.3197390

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{cormode_sigmod18,
        title = {{Privacy at Scale: Local Differential Privacy in Practice}},
        author = {Cormode, Graham and Jha, Somesh and Kulkarni, Tejas and Li, Ninghui and Srivastava, Divesh and Wang, Tianhao},
        series = {{SIGMOD} '18},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3183713.3197390},
        url = {https://dl.acm.org/doi/10.1145/3183713.3197390},
        year = {2018}
}

Incoming Citations (Sorted by Pagerank)

Showing 14 of 14 citing papers.

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Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

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

Rank Cited Paper Year Venue Pagerank
218 Limiting Privacy Breaches in Privacy Preserving Data Mining 2003 PODS 0.00024420564
1,678 Heavy Hitters and the Structure of Local Privacy 2018 PODS 0.00010032135
1,837 Marginal Release Under Local Differential Privacy 2018 SIGMOD 9.6443703e-05
2,862 Privacy via Pseudorandom Sketches 2006 PODS 8.0209471e-05
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