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Aggregate Suppression for Enterprise Search Engines

Summary: Tackles privacy risk of aggregate estimates exposed by keyword-search interfaces in enterprise search. Proposes suppression techniques that preserve per-query quality while limiting sensitive corpus-wide aggregates; theory and experiments. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4608
Venue
SIGMOD
Year
2012
Pagerank
5.093636e-05
Overall Rank
12,309 | 15.55%
DOI
10.1145/2213836.2213890

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Authors

BibTeX Citation

@inproceedings{zhang_sigmod12,
        title = {{Aggregate Suppression for Enterprise Search Engines}},
        author = {Zhang, Mingyang and Zhang, Nan and Das, Gautam},
        series = {{SIGMOD} '12},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2213836.2213890},
        url = {https://dl.acm.org/doi/10.1145/2213836.2213890},
        year = {2012}
}

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Outgoing Citations (Sorted by Pagerank)

Showing 7 of 7 cited papers.

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

Rank Cited Paper Year Venue Pagerank
70 Privacy-Preserving Data Mining 2000 SIGMOD 0.0003804755
1,074 Privacy Preserving OLAP 2005 SIGMOD 0.0001230007
2,401 Simulatable Auditing 2005 PODS 8.6299652e-05
3,296 Towards Robustness in Query Auditing 2006 VLDB 7.5459953e-05
7,903 Mining a Search Engine’s Corpus: Efficient Yet Unbiased Sampling and Aggregate Estimation 2011 SIGMOD 5.5182155e-05
12,384 Randomized Generalization for Aggregate Suppression Over Hidden Web Databases 2011 VLDB 5.093636e-05
12,495 Privacy Preservation of Aggregates in Hidden Databases: Why and How? 2009 SIGMOD 5.093636e-05
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