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
- 2. Nan Zhang
- 3. Gautam Das
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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 |
|---|---|---|---|---|
| 40 | Privacy-Preserving Data Mining | 2000 | SIGMOD | 0.00074232718 |
| 955 | Privacy Preserving OLAP | 2005 | SIGMOD | 0.00015075131 |
| 2,577 | Simulatable Auditing | 2005 | PODS | 8.5099821e-05 |
| 3,258 | Towards Robustness in Query Auditing | 2006 | VLDB | 7.3150323e-05 |
| 7,890 | Mining a Search Engine’s Corpus: Efficient Yet Unbiased Sampling and Aggregate Estimation | 2011 | SIGMOD | 4.6249533e-05 |
| 12,189 | Randomized Generalization for Aggregate Suppression Over Hidden Web Databases | 2011 | VLDB | 4.1945683e-05 |
| 12,301 | Privacy Preservation of Aggregates in Hidden Databases: Why and How? | 2009 | SIGMOD | 4.1945683e-05 |
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