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)
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Authors
- 1. Mingyang Zhang (George Washington University)
- 2. Nan Zhang (George Washington University)
- 3. Gautam Das (University of Texas)
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}
}
Incoming Citations (Sorted by Pagerank)
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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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