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The Tao of Inference in Privacy-Protected Databases

Summary: Introduces the analytically optimal multinomial attack to recover PRE-encrypted attributes, outperforming prior heuristics 16×. Cross-column inference plus ML and record linkage reconstructs semantically encrypted/redacted fields, including medical diagnoses, with up to 97% accuracy. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
11842
Venue
VLDB
Year
2018
Pagerank
0.00010037562
Overall Rank
1,674 | 88.52%
DOI
10.14778/3236187.3236217

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{bindschaedler_vldb18,
        title = {{The Tao of Inference in Privacy-Protected Databases}},
        author = {Bindschaedler, Vincent and Grubbs, Paul and Cash, David and Ristenpart, Thomas and Shmatikov, Vitaly},
        journal = {PVLDB},
        series = {{VLDB} '18},
        volume = {11},
        number = {11},
        pages = {1715--1728},
        doi = {10.14778/3236187.3236217},
        url = {https://doi.org/10.14778/3236187.3236217},
        year = {2018}
}

Incoming Citations (Sorted by Pagerank)

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Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

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

Rank Cited Paper Year Venue Pagerank
310 Order Preserving Encryption for Numeric Data 2004 SIGMOD 0.00021766789
412 Processing Analytical Queries over Encrypted Data 2013 VLDB 0.00018901853
876 Orthogonal Security With Cipherbase 2013 CIDR 0.00013467554
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