Auditing Boolean Attributes
Summary: Audits sum-query databases with Boolean-sensitive attributes; proves continuous-attribute techniques fail and auditing is hard (NP-hard in 2D), so no general efficient solution. Provides an exact 1D range algorithm, a conservative approximate auditor, and methods for max-aggregate variants. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 136 | Revealing Information while Preserving Privacy | 2003 | PODS | 0.0004241101 |
| 304 | On the Complexity of Optimal K-Anonymity | 2004 | PODS | 0.00028290121 |
| 2,119 | Two Can Keep a Secret: A Distributed Architecture for Secure Database Services | 2005 | CIDR | 9.5090272e-05 |
| 7,541 | Privacy-Enhancing k-Anonymization of Customer Data | 2005 | PODS | 4.7157092e-05 |
| 12,352 | Publishing Naive Bayesian Classifiers: Privacy without Accuracy Loss | 2009 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 0 of 0 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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