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Privacy Preservation by Disassociation

Summary: Introduces disassociation for sparse multidimensional data: retain original terms while hiding their co-occurrence in identifying combinations. Unlike generalization, suppression, or differential privacy, it protects against identity disclosure without requiring sensitive/non-sensitive attribute distinctions. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
10721
Venue
VLDB
Year
2012
Pagerank
5.3251649e-05
Overall Rank
9,086 | 37.67%
DOI
10.14778/2350229.2350251

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{terrovitis_vldb12,
        title = {{Privacy Preservation by Disassociation}},
        author = {Terrovitis, Manolis and Liagouris, John and Mamoulis, Nikos and Skiadopoulos, Spiros},
        journal = {PVLDB},
        series = {{VLDB} '12},
        volume = {5},
        number = {10},
        pages = {944--955},
        doi = {10.14778/2350229.2350251},
        url = {https://doi.org/10.14778/2350229.2350251},
        year = {2012}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
10,339 Aegis: A Correlation-Based Data Masking Advisor for Data-Sharing Ecosystems 2026 SIGMOD 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 11 of 11 cited papers.

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

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