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)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
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Showing 5 of 5 cited papers.
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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