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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
4117
Venue
SIGMOD
Year
2009
Pagerank
4.1905499e-05
Overall Rank
12,309 | 14.46%
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
108 WebTables: Exploring the Power of Tables on the Web 2008 VLDB 0.00048345996
957 Privacy Preserving OLAP 2005 SIGMOD 0.00015065499
1,093 Minimal Probing: Supporting Expensive Predicates for Top-k Queries 2002 SIGMOD 0.00014123221
1,486 Distributed Search over the Hidden Web: Hierarchical Database Sampling and Selection 2002 VLDB 0.00011691409
1,538 Google's Deep-Web Crawl 2008 VLDB 0.00011455291
1,853 Information Sharing Across Private Databases 2003 SIGMOD 0.00010323434
2,579 Simulatable Auditing 2005 PODS 8.5010694e-05
3,264 Towards Robustness in Query Auditing 2006 VLDB 7.3073465e-05
5,141 A Random Walk Approach to Sampling Hidden Databases 2007 SIGMOD 5.6627467e-05
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