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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
3702
Venue
SIGMOD
Year
2005
Pagerank
0.0001230007
Overall Rank
1,074 | 92.64%
DOI
10.1145/1066157.1066187

Incoming Non-self Citations Over Time

Authors

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)

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Outgoing Citations (Sorted by Pagerank)

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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
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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