Privacy-MaxEnt: Integrating Background Knowledge in Privacy Quantification

Summary: Privacy-MaxEnt uses maximum entropy to quantify privacy in PPDP, modeling P(SA|QI) as unknowns constrained by background knowledge and published data. It yields the least-biased P(SA|QI) under all constraints, providing a principled privacy metric. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 4008
Venue: SIGMOD
Year: 2008
Pagerank: 4.5386781e-05
Overall Rank: 8,352 | 41.90%
DOI: -

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
2,406	Attacks on Privacy and deFinetti's Theorem	2009	SIGMOD	8.8811954e-05

Outgoing Citations (Sorted by Pagerank)

Showing 7 of 7 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
13	Mining Association Rules between Sets of Items in Large Databases	1993	SIGMOD	0.0010864752
40	Privacy-Preserving Data Mining	2000	SIGMOD	0.00074232718
147	On the Design and Quantification of Privacy Preserving Data Mining Algorithms	2001	PODS	0.00041235556
455	Incognito: Efficient Full-Domain K-Anonymity	2005	SIGMOD	0.00022717354
559	Maintaining Data Privacy in Association Rule Mining	2002	VLDB	0.00020147576
654	Anatomy: Simple and Effective Privacy Preservation	2006	VLDB	0.00018613167
4,509	Privacy Skyline: Privacy with Multidimensional Adversarial Knowledge	2007	VLDB	6.1270304e-05

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