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Privacy Skyline: Privacy with Multidimensional Adversarial Knowledge

Summary: General framework for privacy with external knowledge; introduces a multidimensional adversary knowledge model. A more intuitive, flexible multidimensional privacy criterion; enables faster disclosure measurement and data sanitization. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
9819
Venue
VLDB
Year
2007
Pagerank
6.9431497e-05
Overall Rank
4,037 | 72.31%
DOI
10.14778/1687627.1687715

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{chen_vldb07,
        title = {{Privacy Skyline: Privacy with Multidimensional Adversarial Knowledge}},
        author = {Chen, Bee-Chung and LeFevre, Kristen and Ramakrishnan, Raghu},
        journal = {PVLDB},
        series = {{VLDB} '07},
        volume = {2},
        number = {1},
        pages = {770--781},
        doi = {10.14778/1687627.1687715},
        url = {https://doi.org/10.14778/1687627.1687715},
        year = {2007}
}

Incoming Citations (Sorted by Pagerank)

Showing 8 of 8 citing papers.

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Outgoing Citations (Sorted by Pagerank)

Showing 7 of 7 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
450 Incognito: Efficient Full-Domain K-Anonymity 2005 SIGMOD 0.00018155142
572 Anatomy: Simple and Effective Privacy Preservation 2006 VLDB 0.00016316092
1,088 A Formal Analysis of Information Disclosure in Data Exchange 2004 SIGMOD 0.00012248135
1,669 Injecting Utility into Anonymized Datasets 2006 SIGMOD 0.00010050522
2,397 Personalized Privacy Preservation 2006 SIGMOD 8.6343421e-05
3,845 Checking for k-Anonymity Violation by Views 2005 VLDB 7.0779843e-05
4,159 On the Efficiency of Checking Perfect Privacy 2006 PODS 6.8626218e-05
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