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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
4547
Venue
SIGMOD
Year
2012
Pagerank
4.1905499e-05
Overall Rank
12,120 | 15.77%
DOI
-

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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 0.00074213516
957 Privacy Preserving OLAP 2005 SIGMOD 0.00015065499
2,579 Simulatable Auditing 2005 PODS 8.5010694e-05
3,264 Towards Robustness in Query Auditing 2006 VLDB 7.3073465e-05
7,890 Mining a Search Engine’s Corpus: Efficient Yet Unbiased Sampling and Aggregate Estimation 2011 SIGMOD 4.6205184e-05
12,197 Randomized Generalization for Aggregate Suppression Over Hidden Web Databases 2011 VLDB 4.1905499e-05
12,309 Privacy Preservation of Aggregates in Hidden Databases: Why and How? 2009 SIGMOD 4.1905499e-05
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