To Do or Not To Do: The Dilemma of Disclosing Anonymized Data

Summary: Examines the safety of anonymized data under frequent itemset mining; models attacker priors with belief functions and derives expected cracks across prior classes. Introduces a fast O-estimate heuristic, empirically accurate on real benchmarks, and delivers a practical decision recipe for releasing vs. disclosing risk. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 3624
Venue: SIGMOD
Year: 2005
Pagerank: 4.5386781e-05
Overall Rank: 8,353 | 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 6 of 6 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
136	Revealing Information while Preserving Privacy	2003	PODS	0.0004241101
147	On the Design and Quantification of Privacy Preserving Data Mining Algorithms	2001	PODS	0.00041235556
304	On the Complexity of Optimal K-Anonymity	2004	PODS	0.00028290121
5,089	Secure XML Publishing without Information Leakage in the Presence of Data Inference	2004	VLDB	5.7071771e-05

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