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Privacy Preserving OLAP

Summary: Privacy-preserving OLAP over partitioned multi-client data via randomized perturbation before server aggregation. Formal privacy guarantees from perturbation, reconstruction algorithms for subcube counts on perturbed data, and a practical privacy–accuracy trade-off analysis. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
3640
Venue
SIGMOD
Year
2005
Pagerank
0.00015075131
Overall Rank
955 | 93.36%
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
147 On the Design and Quantification of Privacy Preserving Data Mining Algorithms 2001 PODS 0.00041235556
177 Limiting Privacy Breaches in Privacy Preserving Data Mining 2003 PODS 0.0003788711
559 Maintaining Data Privacy in Association Rule Mining 2002 VLDB 0.00020147576
1,862 Information Sharing Across Private Databases 2003 SIGMOD 0.00010286859
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