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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
3641
Venue
SIGMOD
Year
2005
Pagerank
0.00015065499
Overall Rank
957 | 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 0.00074213516
148 On the Design and Quantification of Privacy Preserving Data Mining Algorithms 2001 PODS 0.00041196325
177 Limiting Privacy Breaches in Privacy Preserving Data Mining 2003 PODS 0.00037858416
559 Maintaining Data Privacy in Association Rule Mining 2002 VLDB 0.0002012549
1,853 Information Sharing Across Private Databases 2003 SIGMOD 0.00010323434
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