DBScholar

Back to papers

IoT-Detective: Analyzing IoT Data Under Differential Privacy

Summary: IoT-Detective adapts PeGaSus DP streaming for real IoT data on UC Irvine's TIPPERS testbed. The demo uses a game to measure usefulness of private streams via visual analysis, exposing application-driven privacy-utility trade-offs in live IoT data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5594
Venue
SIGMOD
Year
2018
Pagerank
5.7387625e-05
Overall Rank
6,922 | 52.51%
DOI
10.1145/3183713.3193571

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{ghayyur_sigmod18,
        title = {{IoT-Detective: Analyzing IoT Data Under Differential Privacy}},
        author = {Ghayyur, Sameera and Chen, Yan and Yus, Roberto and Machanavajjhala, Ashwin and Hay, Michael and Miklau, Gerome and Mehrotra, Sharad},
        series = {{SIGMOD} '18},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3183713.3193571},
        url = {https://dl.acm.org/doi/10.1145/3183713.3193571},
        year = {2018}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 0 of 0 cited papers.

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

Rank Cited Paper Year Venue Pagerank
Previous Page 1 / 1 Next

Semantically Similar Papers