Title
A recursive search algorithm for statistical disclosure assessment
Abstract
A new algorithm, SUDA2, is presented which finds minimally unique itemsets i.e., minimal itemsets of frequency one. These itemsets, referred to as Minimal Sample Uniques (MSUs), are important for statistical agencies who wish to estimate the risk of disclosure of their datasets. SUDA2 is a recursive algorithm which uses new observations about the properties of MSUs to prune and traverse the search space. Experimental comparisons with previous work demonstrate that SUDA2 is several orders of magnitude faster, enabling datasets of significantly more columns to be addressed. The ability of SUDA2 to identify the boundaries of the search space for MSUs is clearly demonstrated.
Year
DOI
Venue
2008
10.1007/s10618-007-0078-6
Data Min. Knowl. Discov.
Keywords
DocType
Volume
Unique itemset,Search space,Algorithm,Recursion,Statistical disclosure
Journal
16
Issue
ISSN
Citations 
2
1384-5810
11
PageRank 
References 
Authors
0.69
29
3
Name
Order
Citations
PageRank
Anna M. Manning1393.97
David J. Haglin211219.45
John A. Keane369592.81