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Towards Robustness in Query Auditing

Summary: Develops efficient online auditors for max and mixed max/min aggregate queries under partial and full disclosure, using probabilistic inference for partial disclosure. Introduces utility analysis for auditing, showing sum-query auditing can preserve answers with few denials on large databases. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
ha190f7492e1f504a
Venue
VLDB
Year
2006
Pagerank
7.3746967e-05
Overall Rank
3,359 | 77.43%
DOI
-

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{nabar_vldb06,
        title = {{Towards Robustness in Query Auditing}},
        author = {Nabar, Shubha U. and Marthi, Bhaskara and Kenthapadi, Krishnaram and Mishra, Nina and Motwani, Rajeev},
        journal = {PVLDB},
        series = {{VLDB} '06},
        year = {2006}
}

Incoming Citations (Sorted by Pagerank)

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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
69 Privacy-Preserving Data Mining 2000 SIGMOD 0.00037940729
122 Revealing Information while Preserving Privacy 2003 PODS 0.00030756232
226 Limiting Privacy Breaches in Privacy Preserving Data Mining 2003 PODS 0.00023973554
520 Practical Privacy: The SuLQ Framework 2005 PODS 0.00016931295
1,097 Privacy Preserving OLAP 2005 SIGMOD 0.00012035305
2,153 Auditing Compliance with a Hippocratic Database 2004 VLDB 8.9479454e-05
2,450 Simulatable Auditing 2005 PODS 8.4450717e-05
2,930 Privacy via Pseudorandom Sketches 2006 PODS 7.8380899e-05
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