FAST: Differentially Private Real-Time Aggregate Monitor with Filtering and Adaptive Sampling

Summary: FAST enables real-time differentially private sharing of aggregate time-series data using filtering and adaptive sampling. Adaptive strategies boost utility under DP for streaming and live data, enabling practical DP analytics on SIGMOD'13 datasets. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 4730
Venue: SIGMOD
Year: 2013
Pagerank: 5.3278085e-05
Overall Rank: 9,033 | 38.03%
DOI: 10.1145/2463676.2465253

Incoming Non-self Citations Over Time

Authors

1. Liyue Fan (Emory University)
2. Li Xiong (Emory University)
3. Vaidy Sunderam (Emory University)

BibTeX Citation

@inproceedings{fan_sigmod13,
        title = {{FAST: Differentially Private Real-Time Aggregate Monitor with Filtering and Adaptive Sampling}},
        author = {Fan, Liyue and Xiong, Li and Sunderam, Vaidy},
        series = {{SIGMOD} '13},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2463676.2465253},
        url = {https://dl.acm.org/doi/10.1145/2463676.2465253},
        year = {2013}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
7,429	Benchmarking the Utility of w-event Differential Privacy Mechanisms - When Baselines Become Mighty Competitors	2023	VLDB	5.6201416e-05

Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
62	Privacy Integrated Queries: An Extensible Platform for Privacy-Preserving Data Analysis	2009	SIGMOD	0.00038970535
757	Differentially Private Aggregation of Distributed Time-Series with Transformation and Encryption	2010	SIGMOD	0.00014306168

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