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Privacy Preservation of Aggregates in Hidden Databases: Why and How?

Summary: Hidden databases with form-like interfaces; protecting sensitive aggregates from leakage through individual tuple results. Techniques to thwart bot sampling to infer aggregates; theoretical analysis plus extensive experiments demonstrate effectiveness. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4116
Venue
SIGMOD
Year
2009
Pagerank
4.1945683e-05
Overall Rank
12,301 | 14.43%
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 0.00074232718
107 WebTables: Exploring the Power of Tables on the Web 2008 VLDB 0.00048377684
955 Privacy Preserving OLAP 2005 SIGMOD 0.00015075131
1,096 Minimal Probing: Supporting Expensive Predicates for Top-k Queries 2002 SIGMOD 0.00014120512
1,492 Distributed Search over the Hidden Web: Hierarchical Database Sampling and Selection 2002 VLDB 0.00011694396
1,537 Google's Deep-Web Crawl 2008 VLDB 0.00011465704
1,862 Information Sharing Across Private Databases 2003 SIGMOD 0.00010286859
2,577 Simulatable Auditing 2005 PODS 8.5099821e-05
3,258 Towards Robustness in Query Auditing 2006 VLDB 7.3150323e-05
5,140 A Random Walk Approach to Sampling Hidden Databases 2007 SIGMOD 5.668209e-05
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