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Fast Data Anonymization with Low Information Loss

Summary: One-dimensional quasi-identifiers enable linear-time heuristics for k-anonymity and l-diversity with meaningful information-loss metrics. Space-mapping generalizes to multi-dimensional data, yielding faster anonymization with lower loss and beating state-of-the-art in time and quality. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
9818
Venue
VLDB
Year
2007
Pagerank
6.5560796e-05
Overall Rank
4,699 | 67.77%
DOI
10.14778/1281192.1281209

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{ghinita_vldb07,
        title = {{Fast Data Anonymization with Low Information Loss}},
        author = {Ghinita, Gabriel and Karras, Panagiotis and Kalnis, Panos and Mamoulis, Nikos},
        journal = {PVLDB},
        series = {{VLDB} '07},
        volume = {1},
        number = {1},
        pages = {758--769},
        doi = {10.14778/1281192.1281209},
        url = {https://doi.org/10.14778/1281192.1281209},
        year = {2007}
}

Incoming Citations (Sorted by Pagerank)

Showing 9 of 9 citing papers.

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Outgoing Citations (Sorted by Pagerank)

Showing 8 of 8 cited papers.

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

Rank Cited Paper Year Venue Pagerank
11 Implementing Data Cubes Efficiently 1996 SIGMOD 0.00071822821
338 Generalizing Data to Provide Anonymity when Disclosing Information 1998 PODS 0.00020731054
384 On the Complexity of Optimal K-Anonymity 2004 PODS 0.00019510305
450 Incognito: Efficient Full-Domain K-Anonymity 2005 SIGMOD 0.00018155142
572 Anatomy: Simple and Effective Privacy Preservation 2006 VLDB 0.00016316092
1,669 Injecting Utility into Anonymized Datasets 2006 SIGMOD 0.00010050522
2,397 Personalized Privacy Preservation 2006 SIGMOD 8.6343421e-05
3,110 Achieving Anonymity via Clustering 2006 PODS 7.7477034e-05
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