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
Authors
- 1. Rakesh Agrawal (IBM)
- 2. Ramakrishnan Srikant (IBM)
- 3. Dilys Thomas (Stanford University)
BibTeX Citation
@inproceedings{agrawal_sigmod05,
title = {{Privacy Preserving OLAP}},
author = {Agrawal, Rakesh and Srikant, Ramakrishnan and Thomas, Dilys},
series = {{SIGMOD} '05},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/1066157.1066187},
url = {https://dl.acm.org/doi/10.1145/1066157.1066187},
year = {2005}
}
Incoming Citations (Sorted by Pagerank)
Showing 15 of 15 citing papers.
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Outgoing Citations (Sorted by Pagerank)
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 |
|---|---|---|---|---|
| 70 | Privacy-Preserving Data Mining | 2000 | SIGMOD | 0.0003804755 |
| 218 | Limiting Privacy Breaches in Privacy Preserving Data Mining | 2003 | PODS | 0.00024420564 |
| 244 | On the Design and Quantification of Privacy Preserving Data Mining Algorithms | 2001 | PODS | 0.00023476901 |
| 1,217 | Maintaining Data Privacy in Association Rule Mining | 2002 | VLDB | 0.00011627624 |
| 1,820 | Information Sharing Across Private Databases | 2003 | SIGMOD | 9.6791131e-05 |
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